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Vesti – Mašinsko učenje

Ukupno: 14, strana 1 od 1

10 Artificial Intelligence (AI) Technologies that will rule 2018

 

 

 

Artificial Intelligence is changing the way we think of technology. It is radically changing the various aspects of our daily life. Companies are now significantly making investments in AI to boost their future businesses. According to a Narrative Science report, just 38% percent of the companies surveys used artificial intelligence in 2016—but by 2018, this percentage will increase to 62%. Another study performed by Forrester Research predicted an increase of 300% in investment in AI this year (2017), compared to last year. IDC estimated that the AI market will grow from $8 billion in 2016 to more than $47 billion in 2020. “Artificial Intelligence” today includes a variety of technologies and tools, some time-tested, others relatively new. 1. Natural Language Generation Saying (or writing) the right words in the right sequence to convey a clear message that can be easily understood by the listener (or reader) can be a tricky business. For a machine, which processes information in an entirely different way than the human brain does, it can be trickier still. Solving this issue has been the key focus of the burgeoning field of Natural Language Generation (NLG) for years beyond counting. Natural language generation, a field which has made great strides of late, has begun to manifest in many areas of our lives. It is currently being used in customer service to generate reports and market summaries. Sample vendors: Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS, Yseop. 2. Speech recognition Transcribe and transform human speech into format useful for computer applications. Currently used in interactive voice response systems and mobile applications. Every day, more and more systems incorporate the transcription and transformation of human language into useful formats suitable for computers. Companies offering speech recognition services include NICE, Nuance Communications, OpenText and Verint Systems. 3. Machine Learning Platforms These days, computers can also learn, and they can be incredibly intelligent! Machine learning is a subdiscipline of computer science and a branch of artificial intelligence. Its goal is to develop techniques that allow computers to learn. By providing algorithms, APIs (application programming interface), development and training tools, big data, applications and other machines, machine learning platforms are gaining more and more traction every day. They’re currently being used in diverse business activities, mainly for prediction or classification. Companies focused in machine learning include Amazon, Fractal Analytics, Google, H2O. ai, Microsoft, SAS, Skytree. 4. Virtual Agents There’s no denying that virtual agents – or “chat bots” (or simply, bots) – are experiencing a tremendous resurgence in interest, and along with that, a rapid advance in innovation and technology. Currently used in customer service and support and as a smart home manager. Some of the companies that provide virtual agents include Amazon, Apple, Artificial Solutions, Assist AI, Creative Virtual, Google, IBM, IPsoft, Microsoft, Satisfi. 5. Decision Management Intelligent machines are capable of introducing rules and logic to artificial intelligence systems and can be used for initial setup/training, ongoing maintenance and tuning. It is used in a wide variety of enterprise applications, assisting in or performing automated decision-making. Some of the companies that provide this are Advanced Systems Concepts, Informatica, Maana, Pegasystems, UiPath. 6. AI-Optimized Hardware Companies are investing heavily in ML/AI with hardware designs intended to greatly accelerate the next generation of applications. Graphics processing units (GPU) and appliances specifically designed and architected to efficiently run AI-oriented computational jobs. Some of the companies focused on AI-Optimized Hardware includes Alluviate, Cray, Google, IBM, Intel, Nvidia. 7. Deep Learning Platforms Deep learning is the fastest growing field and the new big trend in machine learning. A set of algorithms that use artificial neural networks to learn in multi-levels, corresponding to different levels of abstraction. Some of the applications of deep learning are automatic speech recognition, image recognition/Optical character recognition, NLP, and classification/clustering/prediction of almost any entity that can be sensed & digitized. Deep learning platform services providers and suppliers include Deep Instinct, Ersatz Labs, Fluid AI, MathWorks, Peltarion, Saffron Technology, Sentient Technologies. 8. Robotic Process Automation Robotic processes automation is possible thanks to scripts and methods that mimic and automate human tasks to support corporate processes. It is now being used in special situations where it’s too expensive or inefficient to hire humans for a specific job or task. We need to remember artificial intelligence is not meant to replace humans, but to complement their abilities and reinforce human talent. Some of the companies focused on this include Advanced Systems Concepts, Automation Anywhere, Blue Prism, UiPath, WorkFusion. 9. Text Analytics and NLP Natural language processing (NLP) is concerned with the interactions between computers and human (natural) languages. This technology uses text analytics to understand the structure of sentences, as well as their meaning and intention, through statistical methods and machine learning. They are also being use by a huge array of automated assistants and apps to extract unstructured data. Some of the services providers and suppliers of these technologies include Basis Technology, Coveo, Expert System, Indico, Knime, Lexalytics, Linguamatics, Mindbreeze, Sinequa, Stratifyd and Synapsify. 10. Biometrics This technology deals with the identification, measurement and analysis of physical aspects of the body’s structure and form and human behavior. It allows more natural interactions between humans and machines, including interactions related to touch, image, speech and body language recognition. This technology is currently mostly being used for market research. Suppliers of this technologies include 3VR, Affectiva, Agnitio, FaceFirst, Sensory, Synqera and Tahzoo. ORIGINAL                  
 
   

20 najtraženijih knjiga na Sajmu knjiga u Beogradu PO SAJAMSKIM CENAMA

 

 

 

20 najtraženijih knjiga na Sajmu knjiga u Beogradu PO SAJAMSKIM CENAMA Do 17. novembra traje akcija. Ovo je 20 najtraženijih knjiga na našem štandu: Daleko ispred svih je bila knjiga Osnove veštačke inteligencije i mašinskog učenja. Knjigu/e naručujte korišćenjem korpe. 1. OSNOVE VEŠTAČKE INTELIGENCIJE I MAŠINSKOG UČENJA 2. UVOD U PYTHON - AUTOMATIZOVANJE DOSADNIH POSLOVA 3. C# I . NET CORE PROJEKTNI OBRASCI 4. Laravel - Radni okvir za izradu modernih PHP aplikacija 5. GRAPHQL I REACT FULL-STACK VEB RAZVOJ 6. CCNA ROUTING AND SWITCHING 200-125 7. KALI LINUX - TESTIRANJE NEPROBOJNOSTI VEBA 8. SQL ZA ANALIZU PODATAKA 9. C# 7 i . NET CORE 2 MEĐUPLATFORMSKO PROGRAMIRANJE 10. OBJEKTNO-ORIJENTISAN JAVASCRIPT 11. ANDROID 9, KOTLIN I ANDROID STUDIO U JEDNOJ KNJIZI 12. ADMINISTRIRANJE LINUX SISTEMA - KUVAR 13. WORDPRESS 5 U CELOSTI 14. C++ JEDNA LEKCIJA DNEVNO 15. PHP7, MYSQL I JAVASCRIPT U JEDNOJ KNJIZI 16. NODE. JS, MONGODB I ANGULAR za razvoj veb 17. HTML5, CSS3 I JAVASCRIPT ZA RAZVOJ VEB STRANA 18. AMAZON VEB SERVISI U PRAKSI 19. R ANALIZA PODATAKA II IZDANJE 20. ZAŠTITA OD ZLONAMERNIH PROGRAMA 10% dodatnog popusta za 2 ili više knjiga, osim knjiga koje su u pretplati i kompleta knjiga. AKCIJA TRAJE DO 17. NOVEMBRA.  
 
   

Dobro došli na 66. Sajam knjiga u Beogradu

 

 

 

Zadovoljstvo nam je da vas obavestimo da će se Sajam knjiga u Beogradu održati od 21. do 29. oktobra 2023. godine. Kao i prethodne godine, naš štand će biti na istoj lokaciji - na galeriji Hale I. Pokretne stepenice rade. NA SAJMU KNJIGA SAJAMSKE CENE. DODATNI POPUST OD 10% ZA KUPOVINU 2 I VIŠE KNJIGA. O prošlog sajma smo objavili sledeće knjige: 1.  ChatGPT od početnika do profesionalca 2.  JavaScript projektni obrasci, prevod drugog izdanja 3.  Unity 2022 razvoj mobilnih igara 4.  Kotlin za Android aplikacije, prevod 2. izdanja 5.  PowerShell, praktična automatizacija 6.  Python intenzivni kurs, prevod 3. izdanja 7.  Podacijska pismenost (Data Literacy) 8.  AutoCAD 2023, 2D crtanje i 3D modelovanje 9.  Rust veb razvoj 10 Kali Linux: Napredno penetraciono testiranje pomoću alata Nmap, Metasploit, Aircrack-ng i Empire 11.  React i React Native: Izgradnja međuplatformskih JavaScript aplikacija 12.  KOD, skriveni jezik kompjuterskog hardvera i softvera, prevod drugog izdanja 13.  Otkrijte skrivena blaga Microsoft Excela  Tri najtraženije knjige u pretplati ove godine su bile: Python intezivni kurs ChatGPT od početnika do profesionalca React i React native Dodatni popust za 2 i više kupljenih knjiga I ove godine odobravamo 10% dodatnog popusta ukoliko kupite 2 ili više knjiga. Plaćanje Na našem štandu knjige možete da platite karticom ili gotovinom. Spisak svih knjiga OD POSLEDNJE IZAŠLE KNJIGE Hvala vam što kupovinom knjiga podržavate našu izdavačku delatnost. Vidimo se na Sajmu.    
 
   

Learning Machine Learning and NLP from 187 Quora Questions

 

 

 

  Quora has become a great resource for machine learning. Many top researchers are active on the site answering questions on a regular basis. Here are some of the main AI-related topics on Quora. If you have a Quora account, you can subscribe to these topics to customize your feed. Computer-Science (5. 6M followers) Machine-Learning (1. 1M followers) Artificial-Intelligence (635K followers) Deep-Learning (167K followers) Natural-Language-Processing (155K followers) Classification-machine-learning (119K followers) Artificial-General-Intelligence (82K followers) Convolutional-Neural-Networks-CNNs (25K followers) Computational-Linguistics (23K followers) Recurrent-Neural-Networks (17. 4K followers) While Quora has FAQ pages for many topics (e. g. FAQ for Machine Learning), they are far from comprehensive. In this post, I’ve tried to provide a more thorough Quora FAQ for several machine learning and NLP topics. Quora doesn’t have much structure, and many questions you find on the site are either poorly answered or extremely specific. I’ve tried to include only popular questions that have good answers on general interest topics. Machine Learning How do I learn machine learning? What is machine learning? What is machine learning in layman’s terms? What is the difference between statistics and machine learning? What machine learning theory do I need to know in order to be a successful machine learning practitioner? What are the top 10 data mining or machine learning algorithms? What exactly is a “hyperparameter” in machine learning terminology? How does a machine-learning engineer decide which neural network architecture (feed-forward, recurrent or CNN) to use to solve their problem? What’s the difference between gradient descent and stochastic gradient descent? How can I avoid overfitting? What is the role of the activation function in a neural network? What is the difference between a cost function and a loss function in machine learning? What is the difference between a parametric learning algorithm and a nonparametric learning algorithm? What is regularization in machine learning? What is the difference between L1 and L2 regularization? What is the difference between Dropout and Batch Normalization? What is an intuitive explanation for PCA? When and where do we use SVD? What is an intuitive explanation of the relation between PCA and SVD? Which is your favorite Machine Learning algorithm? What is the future of machine learning? What are the Top 10 problems in Machine Learning for 2017? Classification What are the advantages of different classification algorithms? What are the advantages of using a decision tree for classification? What are the disadvantages of using a decision tree for classification? What are the advantages of logistic regression over decision trees? How does randomization in a random forest work? Which algorithm is better for non linear classification? What is the difference between Linear SVMs and Logistic Regression? How can l apply an SVM for categorical data? How do I select SVM kernels? How is root mean square error (RMSE) and classification related? Why is “naive Bayes” naive? Regression How would linear regression be described and explained in layman’s terms? What is an intuitive explanation of a multivariate regression? Why is logistic regression considered a linear model? Logistic Regression: Why sigmoid function? When should we use logistic regression and Neural Network? How are linear regression and gradient descent related? What is the intuition behind SoftMax function? What is softmax regression? Supervised Learning What is supervised learning? What does “supervision” exactly mean in the context of supervised machine learning? Why isn’t supervised machine learning more automated? What are the advantages and disadvantages of a supervised learning machine? What are the main supervised machine learning methods? What is the difference between supervised and unsupervised learning algorithms? Reinforcement Learning How do I learn reinforcement learning? What’s the best way and what are the best resources to start learning about deep reinforcement learning? What is the difference between supervised learning and reinforcement learning? How does one learn a reward function in Reinforcement Learning (RL)? What is the Future of Deep Reinforcement Learning (DL + RL)? Is it possible to use reinforcement learning to solve any supervised or unsupervised problem? What are some practical applications of reinforcement learning? What is the difference between Q-learning and R-learning? In what way can Q-learning and neural networks work together? Unsupervised Learning Why is unsupervised learning important? What is the future of deep unsupervised learning? What are some issues with Unsupervised Learning? What is unsupervised learning with example? Why could generative models help with unsupervised learning? What are some recent and potentially upcoming breakthroughs in unsupervised learning? Can neural networks be used to solve unsupervised learning problems? What is the state of the art of Unsupervised Learning, and is human-likeUnsupervised Learning possible in the near future? Why is reinforcement learning not considered unsupervised learning? Deep Learning What is deep learning? What is the difference between deep learning and usual machine learning? As a beginner, how should I study deep learning? What are the best resources to learn about deep learning? What is the difference between deep learning and usual machine learning? What’s the most effective way to get started with Deep Learning? Is there something that Deep Learning will never be able to learn? What are the limits of deep learning? What is next for deep learning? What other ML areas can replace deep learning in the future? What is the best back propagation (deep learning) presentation for dummies? Does anyone ever use a softmax layer mid-neural network rather than at the end? What’s the difference between backpropagation and backpropagation through time? What is the best visual explanation for the back propagation algorithm for neural networks? What is the practical usage of batch normalization in neural networks? In layman’s terms, what is batch normalisation, what does it do, and why does it work so well? Does using Batch Normalization reduce the capacity of a deep neural network? What is an intuitive explanation of Deep Residual Networks? Is fine tuning a pre-trained model equivalent to transfer learning? What would be a practical use case for Generative models? Is cross-validation heavily used in Deep Learning or is it too expensive to be used? What is the importance of Deep Residual Networks? Where is Sparsity important in Deep Learning? Why are Autoencoders considered a failure? In deep learning, why don’t we use the whole training set to compute the gradient? Convolutional Neural Networks What is a convolutional neural network? What is an intuitive explanation for convolution? How do convolutional neural networks work? How long will it take for me to go from machine learning basics to convolutional neural network? Why are convolutional neural networks well-suited for image classification problems? Is a pooling layer necessary in CNN? Can it be replaced by convolution? How can the filters used in Convolutional Neural Networks be optimized or reduced in size? Is the number of hidden layers in a convolutional neural network dependent on size of data set? How can convolutional neural networks be used for non-image data? Can I use Convolution neural network to classify small number of data, 668 images? Why are CNNs better at classification than RNNs? What is the difference between a convolutional neural network and a multilayer perceptron? What makes convolutional neural network architectures different? What’s an intuitive explanation of 1x1 convolution in ConvNets? Why does the convolutional neural network have higher accuracy, precision, and recall rather than other methods like SVM, KNN, and Random Forest? How can I train Convolutional Neural Networks (CNN) with non symmetric images of different sizes? How can l choose the dimensions of my convolutional filters and pooling in convolutional neural network? Why would increasing the amount of training data decrease the performance of a convolutional neural network? How can l explain that applying max-pooling/subsampling in CNN doesn’t cause information loss? How do Convolutional Neural Networks develop more complex features? Why don’t they use activation functions in some CNNs for some last convolution layers? What methods are used to increase the inference speed of convolutional neural networks? What is the usefulness of batch normalization in very deep convolutional neural network? Why do we use fully connected layer at the end of a CNN instead of convolution layers? What may be the cause of this training loss curve for a convolution neural network? The convolutional neural network I’m trying to train is settling at a particular training loss value and a training accuracy just after a few epochs. What can be the possible reasons? Why do we use shared weights in the convolutional layers of CNN? What are the advantages of Fully Convolutional Networks over CNNs? How is Fully Convolutional Network (FCN) different from the original Convolutional Neural Network (CNN)? Recurrent Neural Networks Artificial Intelligence: What is an intuitive explanation for recurrent neural networks? How are RNNs storing ‘memory’? What are encoder-decoder models in recurrent neural networks? Why do Recurrent Neural Networks (RNN) combine the input and hidden state together and not seperately? What is an intuitive explanation of LSTMs and GRUs? Are GRU (Gated Recurrent Unit) a special case of LSTM? How many time-steps can LSTM RNNs remember inputs for? How does attention model work using LSTM? How do RNNs differ from Markov Chains? For modelling sequences, what are the pros and cons of using Gated Recurrent Units in place of LSTMs? What is exactly the attention mechanism introduced to RNN (recurrent neural network)? It would be nice if you could make it easy to understand! Is there any intuitive or simple explanation for how attention works in the deep learning model of an LSTM, GRU, or neural network? Why is it a problem to have exploding gradients in a neural net (especially in an RNN)? For a sequence-to-sequence model in RNN, does the input have to contain only sequences or can it accept contextual information as well? Can “generative adversarial networks” be used in sequential data in recurrent neural networks? How effective would they be? What is the difference between states and outputs in LSTM? What is the advantage of combining Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN)? Which is better for text classification: CNN or RNN? How are recurrent neural networks different from convolutional neural networks? Natural Language Processing As a beginner in Natural Language processing, from where should I start? What is the relation between sentiment analysis, natural language processing and machine learning? What is the current state of the art in natural language processing? What is the state of the art in natural language understanding? Which publications would you recommend reading for someone interested in natural language processing? What are the basics of natural language processing? Could you please explain the choice constraints of the pros/cons while choosing Word2Vec, GloVe or any other thought vectors you have used? How do you explain NLP to a layman? How do I explain NLP, text mining, and their difference in layman’s terms? What is the relationship between N-gram and Bag-of-words in natural language processing? Is deep learning suitable for NLP problems like parsing or machine translation? What is a simple explanation of a language model? What is the definition of word embedding (word representation)? How is Computational Linguistics different from Natural Language Processing? Natural Language Processing: What is a useful method to generate vocabulary for large corpus of data? How do I learn Natural Language Processing? Natural Language Processing: What are good algorithms related to sentiment analysis? What makes natural language processing difficult? What are the ten most popular algorithms in natural language processing? What is the most interesting new work in deep learning for NLP in 2017? How is word2vec different from the RNN encoder decoder? How does word2vec work? What’s the difference between word vectors, word representations and vector embeddings? What are some interesting Word2Vec results? How do I measure the semantic similarity between two documents? What is the state of the art in word sense disambiguation? What is the main difference between word2vec and fastText? In layman terms, how would you explain the Skip-Gram word embedding model in natural language processing (NLP)? In layman’s terms, how would you explain the continuous bag of words (CBOW) word embedding technique in natural language processing (NLP)? What is natural language processing pipeline? What are the available APIs for NLP (Natural Language Processing)? How does perplexity function in natural language processing? How is deep learning used in sentiment analysis? Generative Adversarial Networks Was Jürgen Schmidhuber right when he claimed credit for GANs at NIPS 2016? Can “generative adversarial networks” be used in sequential data in recurrent neural networks? How effective would they be? What are the (existing or future) use cases where using Generative Adversarial Network is particularly interesting? Can autoencoders be considered as generative models? Why are two separate neural networks used in Generative Adversarial Networks? What is the advantage of generative adversarial networks compared with other generative models? What are some exciting future applications of Generative Adversarial Networks? Do you have any ideas on how to get GANs to work with text? In what way are Adversarial Networks related or different to Adversarial Training? What are the pros and cons of using generative adversarial networks (a type of neural network)? Can Generative Adversarial networks use multi-class labels? Robbie Allen  Startup Founder & Author turned PhD Student @UNCCS focused on Artificial Intelligence. Founder & Chairman @AInsights. Writing at http://unsupervisedmethods. com Jul 28 ORIGINAL
 
   

Na današnji dan, 13. marta

 

 

 

Iz istorije računarstva: Na današnji dan, 13. marta 1986. godine, Microsoft, kompanija osnovana skoro jedanaest godina ranije, izašla je na berzu NASDAQ. Početna cena deonice bila je 28 dolara po deonici, čime je kompanija za samo jedan dan prikupila gotovo 61 milion dolara. Da ste 13. marta 1986. kupili deonicu po ceni od 21 dolar, vaša investicija bi 2012. godine vredela približno 4. 000 dolara. ------ 1325. - Asteci su osnovali svoju naseobinu Tenočtitlan, kasnije prestonicu Asteškog carstva, na mestu gde je današnji grad Sijudad Meksiko. 1567. - Nemački plaćenici koje je unajmila holandska vladarka Margareta od Parme ubili su 2. 000 kalvinista. 1572. - U Starigradu na ostrvu Hvar umro je Petar Hektorović, autor speva "Ribanje i ribarsko prigovaranje", izuzetno vrednog književno-istorijskog dokumenta, u kojem je Hektorović, pored autobiografskih zabeležio i dragocene etnografske podatke, kao i neke narodne lirske i epske pesme, među kojima i srpsku narodnu pesmu "Kraljević Marko i brat mu Andrijaš". Preveo je spev "Remedia amoris" rimskog pesnika Ovidija (Ovidius). 1781. - Engleski astronom nemačkog porekla Vilijam Heršel (William Hershel) otkrio je sedmu planetu Sunčevog sistema, koja je kasnije nazvana Uran. 1809. - Posle neuspeha u ratu s Rusijom i Danskom 1808, oficirskom zaverom zbačen je s prestola švedski kralj Gustav IV. 1848. - Pod pritiskom demonstracija i pobune u Beču austrijski kancelar Klemens Meternih (Metternich) podneo je ostavku. Kancelar je pobegao u Veliku Britaniju, a hiljadama gnevnih Bečlija koje su opkolile dvor car Ferdinand I je obećao ustav. 1865. - Tokom Američkog građanskog rata Kongres Konfederacije, pod predsednikom Džefersonom Dejvisom (Jefferson Devis), žestokim protivnikom ukidanja ropstva, doneo je zakon kojim je robovima dozvoljeno, u zamenu za slobodu, da budu vojnici u južnjačkoj armiji. 1881. - U atentatu u Petrogradu ubijen je ruski car Alksandar II. Atentat su izvršili članovi tajnog terorističkog udruženja "Narodna volja". 1906. - Umrla je Suzan Entoni (Susan Anthony), začetnica i vođa pokreta za prava žena u SAD. Napisala je "Istoriju ženskog prava glasa". 1913. - Kanbera je postala glavni grad Australije. 1928. - Posle pucanja brane "St. Frensis", oko 60 kilometara severno od Los Anđelesa, u vodi koja je preplavila dolinu utopilo se više od 450 ljudi. 1946. - Pripadnici jugoslovenske službe Državne bezbednosti uhapsili su generala i komandanta kraljevske vojske u Drugom svetskom ratu Dragoljuba Dražu Mihailovića. Na suđenju pred Vojnim sudom u Beogradu Mihailović je osuđen na smrt zbog izdaje. Streljan je 17. jula iste godine. 1972. - Velika Britanija i Kina saglasile su se da razmene ambasadore 22 godine pošto je London priznao vladu u Pekingu; Britanci su zatvorili konzulat na Tajvanu. 1975. - Umro je Ivo Andrić, jedan od najvećih jugoslovenskih pisaca 20. veka, čije je delo donelo ugled i međunarodno priznanje jugoslovenskoj književnosti. On je jedini jugoslovenski književnik dobitnik Nobelove nagrade za književnost (1961) ("Edž ponto", "Znakovi", "Na Drini ćuprija", "Travnička hronika", "Gospođica", "Prokleta avlija").   Znakovi pored puta Ova "knjige mudrosti", "vrsta intimnih dnevnika", koje je Andrić pisao celog života, smatraju se jedinstvenim u našoj književnosti i stoga jer je pisac u njima prvi put nedvosmisleno progovorio o sebi. Naručite knjigu i pratite znakove Beogradske priče Oslobodimo li se predrasuda, ostajući na tragu osobenosti Andrićevog pripovedačkog postupka, možemo sklopiti skicu jedne posebne literarne građevine, takozvane beogradske hronike. PORUČITE Priče o deci Svaka od ovih priča dirljiva je apoteoza dečijem mikrokosmosu i izraz života u jednom individualnom svetu, dokaz bogatstva toga sveta, njegove angažovanosti ili pasivnosti, agresivnosti ili produhovljenosti. PORUČITE Priče o moru Prizori kamenih zidina starih gradova ovenčanih mediteranskim rastinjem, večna muzika šuma talasa, škrgutanje šljunka, zveckanje zagonetnih nanosa plime, miris morskog vazduha. . . PORUČITE Priče o osobenjacima i malim ljudima Priče o osobenjacima i malim ljudima otkrivaju Andrićevo pripovedačko umeće da se kloni stereotipa i da svoje junake pronalazi u svetu drugačijem i različitom od onog koji ih okružuje. PORUČITE Sarajevske priče Na istorijskoj pozadini burne 1878. godine, društvenih previranja 1906. godine ili razaranja tokom Drugog svetskog rata, Andrić prati svoje junake, njihove pojedinačne, uzbudljive i najčešće tragične sudbine, te promene na emotivnoj mapi njihovog postojanja. PORUČITE Turske priče Orijent je divno čudo i najveći užas, jer u njemu granica između smrti i života nije jasno određena, nego krivuda i treperi. PORUČITE   1990. - Sovjetski parlament izglasao je uvođenje višepartijskog sistema, nakon 72-godišnjeg monopola na vlast Komunističke partije. 1992. - U zemljotresu na istoku Turske je poginulo najmanje 570 ljudi. 1995. - U Beogradu je u 72. godini umro popularni pozorišni i filmski glumac Mija Aleksić. 1996. - U mestu Danblejn, oko 40 kilometara severno od Glazgova, naoružani čovek je u gimnastičkoj sali osnovne škole ubio 16 učenika prvog razreda, uzrasta između pet i šest godina i njihovu učiteljicu, ranio još 13 đaka i potom izvršio samoubistvo. 1998. - Predsednik Južne Koreje Kim Dae Džong, koji je i sam bio zatvaran zbog političkih uverenja, doneo je odluku o masovnoj amnestiji koja je obuhvatila preko pet miliona osoba, od političkih zatvorenika do pijanih vozača kojima su bile oduzete vozačke dozvole. 1999. - Na Kosovu, u eksplozijama bombi u centru Podujeva i na pijaci u Kosovskoj Mitrovici poginulo je šestoro i ranjeno više od 50 ljudi, a u napadima oružane formacije kosovskih Albanaca "Oslobodilačka vojska Kosova" kod Vučitrna poginula su dva pripadnika Vojske Jugoslavije. 2001. - Bivši gradonačelnik Bosanskog Šamca Blagoje Simić, protiv koga je Međunarodni sud za ratne zločine podigao optužnicu 1995. za zločine počinjene tokom rata u BiH 1992-95, dobrovoljno se predao sudu. 2002. - Vlada Angole je proglasila jednostrano primirje u 27-godišnjem građanskom ratu sa pobunjenicima-pripadnicima Nacionalne Unije za nezavisnost Angole (UNITA). 2004. - Umro je austrijski kardinal Franc Kenig (Franz Koenig), poznat po zalugama u povezivanju vera. On je godinama radio na uspostavljanju veza između Vatikana i komunističkih država. Na njegovu inicijativu 1964. godine osnovana je Fondacija "Pro orijente", koja se bavi dijalogom između rimokatoličke i pravoslavne crkve.   2004. - Umrla Marina Koljubajeva, filmska i TV glumica (Beograd, 02. 11. 1950 - Beograd, 13. 03. 2004) 2005. - Umro Aleksandar Atanacković, fudbaler, reprezentativac (Beograd, 29. 04. 1920 - Beograd 13. 03. 2005) 2008. - Umro Živko Šoklovački, pravnik, političar, šef poslaničke grupe Jugoslovenske levice u Skupštini SFRJ, predsednik Upravnog Odbora NIS-a (Kikinda, 1946 - Novi Sad, 13. 03. 2008) IZVOR.
 
   

Na današnji dan, 15. februara Sretenjski ustav i džez pevač Kol

 

 

 

Na današnji dan, 15. februara 1934. godine, rođen je Nikolaus Virt (Niklaus Wirth), čuveni švajcarski informatičar i pronalazač programskog jezika Pascal. Njegovo mesto rođenja je Vinterur, grad u Švajcarskoj. Virtova akademska karijera počinje diplomiranjem na Univerzitetu Laval 1960. godine, nakon čega nastavlja da gradi svoje obrazovanje na Univerzitetu u Kaliforniji, Berkli, gde je 1963. godine doktorirao. U periodu između 1963. i 1967. godine, Virt je bio član akademskog osoblja na Univerzitetu Stanford, gde je predavao i delio svoja znanja i iskustva sa studentima. Nakon toga, 1967. godine, prelazi na Univerzitet u Cirihu, gde je do 1975. godine obavljao funkciju profesora računarskih nauka. Svoju akademsku karijeru nastavio je 1975. godine na prestižnom Saveznom institutu za tehnologiju u Cirihu, gde je takođe predavao računarske nauke. Virt je posebno poznat po svom doprinosu u razvoju programskih jezika, među kojima se posebno ističe Pascal, jezik koji je značajno uticao na oblast računarstva i programiranja. Njegov rad na ovom jeziku i drugim projektima u oblasti informatike značajno je doprineo razvoju i napretku računarskih tehnologija. -------- 1115. - Papa Paskal II priznao je red Malteških vitezova osnovanu Jerusalimu, gde su pored crkve Svetog Jovana imali samostan i azil za bolesnike i putnike. Kasnije su dobili zadatak da brane Svetu zemlju. 1573. - U Zagrebu je, nakon sloma seljačke bune, pogubljen vođaustanka Matija Gubec. 1763. - Mirovnim ugovorom u Hubertsburgu okončana suneprijateljstva Austrije i Pruske u Sedmogodišnjem ratu. Pruska je zadržala Šleziju koju je preuzela od Austrije i postala je jedna od vodećih evropskih vojnih sila. 1835. - U Kneževini Srbiji usvojen je Sretenjski ustav, prviustav u modernoj srpskoj istoriji. Napisao ga je sekretar kneza Miloša Obrenovića Dimitrije Davidović, po uzoru na francuski i belgijski ustav. Koristeći negodovanje Austrije i Rusije, koje nisu imale razumevanja za liberalizam Davidovićevog dokumenta, knez je u martu ukinuo ustav. 1857. - Umro je ruski kompozitor Mihail Ivanovič Glinka, tvoracruskog nacionalnog stila romantičarske epohe. Njegove opere "Život za cara" i "Ruslan i Ljudmila" imale su značajan uticaj na razvoj ruske opere u 19. veku. 1922. - U Hagu je održana prva sednica stalnog Međunarodnog sudapravde koji je 1920. osnovala Liga naroda radi rešavanja sporova među državama. Novi Međunarodni sud osnovan je pri Ujedinjenim nacijama posle Drugog svetskog rata kada je ukinuta Liga naroda. 1928. - Iz Beograda prema Zagrebu poleteo je prvi avion jugoslovenske civilne avijacije "Potez 29-2", jedina letelica prve jugoslovenske avio-kompanije "Aeroput". Prvi putnici bili su direktor avio-kompanije i pet novinara. 1942. - Umro je srpski kompozitor i dirigent Stanislav Binički,autor "Marša na Drinu", direktor Beogradske opere (1920-25). Godine 1899. osnovao je Beogradski vojni orkestar, a sa Stevanom Mokranjcem Srpsku muzičku školu. Komponovao je prvu izvedenu srpsku operu "Na uranku" (1903), na tekst Branislava Nušića. 1942. - Procenivši da nisu u stanju da odbrane grad, Britanci suu Drugom svetskom ratu predali Singapur japanskim snagama. To se smatra najvećim vojnim porazom Velike Britanije u njenoj vojnoj istoriji. 1944. - Američke trupe su u Drugom svetskom ratu zauzeleSolomonska ostrva u Pacifiku. Britanski avioni izbacili su oko 1. 000 bombi na Berlin. 1952. - Rođen je Tomislav Nikolić. Tomislav Nikolić (Kragujevac, 15. februar 1952) je srpski političar, predsednik Republike Srbije, osnivač i prvi predsednik Srpske napredne stranke, bivši predsednik Narodne skupštine Republike Srbije i bivši potpredsednik vlade Srbije i Savezne Republike Jugoslavije. [1] Jedan je od osnivača Srpske radikalne stranke, čiji je bio potpredsednik, a kasnije i zamenik predsednika, Vojislava Šešelja. Kada se Šešelj februara 2003. godine dobrovoljno predao Haškom tribunalu, preuzeo je vođstvo Radikalne stranke. Tri puta kandidat na predsedničkim izborima u Srbiji (2003, 2004, 2008), i jednom kandidat za predsednika SR Jugoslavije (2000). Na predsedničkim izborima u Srbiji 2012. godine je odneo pobedu nad kandidatom Demokratske stranke, Borisom Tadićem. 1965. - Umro je popularni američki džez pevač i pijanist Nat King Kol (Cole). 1971. - Velika Britanija je prešla na decimalni novčani sistem,umesto dotadašnjih funti, šilinga i penija. 1988. - Predsednik Austrije Kurt Valdhajm (Waldheim), optužen daje, kao pripadnik Š jedinica u Drugom svetskom ratu, odgovoran za ratne zločine počinjen u Bosni i Grčkoj, odbio je da podnese ostavku. 1989. - Deset godina nakon što je Moskva poslala svoje trupe dapodrže prokomunističku vladu u Kabulu, poslednji sovjetski vojnici napustili su Avganistan. U desetogodišnjem neobjavljenom ratu poginulo je 15. 000 sovjetskih vojnika i najmanje 100. 000 Avganistanaca. 1990. - Velika Britanija i Argentina obnovile su diplomatskeodnose, prekinute 1982. u vreme rata za Foklandska ostrva. 1993. - Slovački parlament izabrao je ekonomistu Mihala Kovača(Michal Kovac) za prvog predsednika novoformirane države Slovačke, nakon raspada Čehoslovačke. 1996. - Bosanska vlada je saopštila da je u ratu 1992-95. nestalooko 30. 000 ljudi, od čega preko 22. 000 civila i oko 2. 500 pripadnika oružanih snaga. 1997. - Srpska opoziciona koalicija "Zajedno" održala jeposlednji, 88. dan protesta pošto je 11. februara ispunjen njen osnovni zahtev - priznavanje rezultata lokalnih izbora u Srbiji iz novembra 1996. 1999. - U Keniji je uhapšen lider kurdskih pobunjenika AbdulahOdžalan (Ocalan) i isporučen Turskoj, gde je osuđen na smrt zbog izdaje i prebačen u ostrvski zatvor Imrali. 2002. - Predsednik SAD Džordž Buš (Georges Bush) objavio je danjegova zemlja ima alternativni plan za smanjenje emisije gasova, koje je regulisano Protokolom o globalnom zagrevanju u Kjotou 1997. Iako emituju četvrtinu količine gasova koji stvaraja efekat "staklene bašte", SAD su odbacile Protokol iz Kjotoa zbog štete po nacionalnu industriju. 2003. - U talasu demonstracija, najvećim od vijetnamskog rata,više od šest miliona ljudi, u više od 600 gradova u svetu, protestovalo je protiv rata u Iraku. 2003. - Vatikan je otvorio zapečaćenu arhivu o vezama te državesa Nemačkom između 1922. i 1939. godine, kada je Euđenio Pačeli (kasnije Papa Pije XII), bio državni sekretar Vatikana. Otvaranje arhiva usledilo je kao odgovor kritičarima Pape da nije dovoljno učinio da zaustavi ubijanje miliona Jevreja od strane nacističke Nemačke tokom holokausta. 2004. - U Kini je pogubljen Jang Ksinhai (Yang Xinhai), jedan odnajvećih serijskih ubica, čovek koji je za četiri godine ubio 67 osoba i silovao 12 žena. 2012. - Google je promenio logo u čast Srbije: GOOGLE - СРБИЈА. 2014. Google i Dan državnosti 2018. Gugl dudl i Dan državnosti Srbije
 
   

Na današnji dan, 16. februara Radoje Domanović i Isidora Sekulić

 

 

 

Na današnji dan, 16. februara 2016. godine, Apple je odbio sudski nalog za kreiranje "backdoor" pristupa u iPhone koji je koristio napadač iz San Bernardina, Syed Farook. U danima koji su usledili, generalni direktor Apple-a Tim Cook izjavio je da kompanija "nema toleranciju niti simpatije prema teroristima", ali da je "na kocki sigurnost podataka stotina miliona zakonitih ljudi i uspostavljanje opasnog presedana koji preti građanskim slobodama svih nas". FBI je na kraju uspeo da otključa telefon bez pomoći kompanije Apple.   ------- 1871. - Rođen je srpski pisac Radoje Domanović, najveći srpski satiričar, Od 1905. do smrti 1908. bio je šef korektora Državne štamparije u Beogradu. Uređivao je satirični list "Stradija". Dela: "Stradija", "Vođa", "Danga", "Mrtvo more", "Kraljević Marko po drugi put među Srbima". 1877. - Rođena je srpska književnica Isidora Sekulić, član Srpske akademije nauka i umetnosti. Kritičari je smatraju klasikom srpske književnosti. Završila je Viši pedagogijum u Budimpešti i doktorirala u Nemačkoj. Bila je nastavnica i upravnica devojačke škole u Pančevu, zatim profesor gimnazije u Beogradu. Bila je poynavalac mnogih jezika i izvrstan prevodilac, a naročito je mnogo prevodila sa engleskog. Dela: putopis "Pisma iz Norveške", roman "Đakon Bogorodičine crkve", pripovetke "Hronika palanačkog groblja", "Saputnici", "Gospa Nola", eseji "Analitički trenuci i teme", "Zapisi o mome narodu", "Mir i nemir", "Njegošu - knjiga duboke odanosti", "Govor i jezik - kulturna smotra naroda". 1926. - Rodjen je Džon Šlezindžer ili Šlezinger (John Schlesinger), američki filmski režiser. 1933. - U strahu od sve češćih nemačkih pretnji, Jugoslavija, Čehoslovačka i Rumunija reorganizovale su odbrambeni savez Malu Antantu, koja je dobila stalni Savet sastavljen od ministara inostranih poslova. Nemačka okupacija Čehoslovačke u martu 1939. praktično je ugasila savez, koji je saradnjom Jugoslavije i Rumunije još neko vreme životario, ali je sa slomom Jugoslavije u Drugom svetskom ratu, posle napada nacističke Nemačke u aprilu 1941, definitivno prestao da postoji. 1937. - Potenstiran je najlon, orig. nylon, novi veštački materijal koji je brzo ušao u široku upotrebu. 1940. - Britanski razarači napali su "Altmark", nemački ratni brod, u jednom norveškom fjordu i oslobodili oko 300 zarobljenih engleskih moranara. Nemci su objavili da su Englezi povredili norvešku neutralnost i izvršili agresivan napad na Norvešku. 1945. - Prvi put je fotografisan Uranov mesec Miranda. 1946. - Testiran je prvi helikopter u komercijalne svrhe. 1959. - Fidel Kastro proglasio je sebe premijerom Kube. Kastro je rođen 1927. godine, a već sa dvadeset godina učestvovao je u pokušaju obaranja diktature Truhilja u Dominikanskoj Republici. Kada se Batista 1952. godine proglasio za kubanskog diktatora, Kastro je digao ustanak u provinciji Orijente, ali je uhapšen i osuđen na 15 godina robije. Već 1955. je pomilovan i sa grupom od 80 demokratskih pristalica osniva Pokret za borbu za politička i socijalna prava kubanskog naroda. Pokret se omasovio u celoj Kubi i vodio borbu portiv Batiste do njegovog beksta iz Kube 1. januara 1959. godine, kada je Kastro preuzeo vlast na celoj teritoriji. Kastro je i danas na vlasti u Kubi, prvoj komunističkoj zemlji u zapadnoj hemisferi. 1961. - Kina je upotrebila svoj prvi nuklearni reaktor. 1994. - Ruske trupe se pridružuju snagama Ujedinjenih nacija u ratom zahvaćenim oblastima bivše Jugoslavije.   IZVOR.  
 
   

Na današnji dan, 23. februara Kits i Zola

 

 

 

Na današnji dan, 23. februara 1905. godine, u Berkliju, Kalifornija, rođen je Derik Lemer (Derrick Lehmer), jedan od svetski poznatih teoretičara brojeva, posebno u oblasti proučavanja prostih brojeva. Pre Drugog svetskog rata, Lemer je razvio niz elektromehaničkih uređaja, poznatih kao Lemerova sita, koja su bila namenjena za efikasno pronalaženje prostih brojeva. Ovi uređaji su bili među prvim elektromehaničkim alatima koji su se koristili u matematičkim istraživanjima, a njihova efikasnost u pronalaženju prostih brojeva bila je revolucionarna za to vreme. Lemerovi doprinosi teoriji prostih brojeva su bili mnogobrojni i značajni, obuhvatajući ne samo razvoj matematičkih alata i tehnika, već i dublje teoretsko razumevanje prirode prostih brojeva. Prosti brojevi su, po definiciji, brojevi veći od 1 koji nemaju drugih delilaca osim 1 i samih sebe. Ova jednostavna definicija krije izuzetno kompleksno i fascinantno polje istraživanja koje ima ključnu ulogu u modernoj matematici i tehnologiji, posebno u kriptografiji, gde se veliki prosti brojevi koriste za enkripciju podataka.   ------------ 1574. - U Francuskoj je izbio peti verski rat između katolika i hugenota (protestanata). Hugenotski ratovi potresali su Francusku do početka 18. veka, a punu ravnopravnost hugenoti su stekli tek nakon Francuske revolucije 1789. 1685. - Rođen je nemački kompozitor Georg Fridrih Hendl (Friedrich Handel), uz Johana Sebastijana Baha (Johann Sebastian Bach) najznačajniji muzičar baroka. U njegovom obimnom opusu najvrednijim se smatraju opere i oratorijumi ("Rinaldo", "Julije Cezar", "Mesija", "Izrael u Egiptu", "Juda Makabejac"). Po odlasku u London postao je centralna ličnost muzičkog života engleske prestonice, a 1719. poverena mu je organizacija i vođenje Kraljevske muzičke akademije. 1792. - Umro je engleski slikar Džošua Rejnolds (Joshua Reynolds), jedan od najvećih svetskih portretista, osnivač Kraljevske umetničke akademije 1768. i njen prvi predsednik. Snažno je uticao na englesko slikarstvo svojim stvaralaštvom i teoretskim raspravama o slikarstvu. 1821. - U Rimu je umro engleski pesnik Džon Kits (John Keats), čija se dela - soneti, spev "Endimion", pesme "Oda slavuju", "Oda grčkoj urni", "Oda jeseni" ubrajaju među najlepša poetska dela na engleskom jeziku. 1836. - Oko 4. 000 vojnika pod komandom meksičkog generala Antonija Lopesa de Santa Ane (Antonio Lopez, Anna) počelo je opsadu tvrđave Alamo u Teksasu. Tvrđava, koju je branilo oko 200 dobrovoljaca, među njima i Dejvi Kroket (Davy Crockett), pala je 6. marta, a svi branioci su izginuli. 1866. - Pod pritiskom bojara nezadovoljnih demokratskim reformama, rumunski knez Aleksandar Kuza (Alexander Cuza), ujedinitelj Moldavije i Vlaške primoran je da abdicira i da napusti zemlju. Nasledio ga je Karol I (Carol), princ od Hoencolerna (Hohenzollern), koji je 1881. postao prvi rumunski kralj. 1898. - Francuski pisac Emil Zola (Emile) uhapšen je zbog objavljivanja otvorenog pisma predsedniku Francuske, pod naslovom "Optužujem", u kojem je vladu optužio za antisemitizam i montirani sudski proces protiv kapetana Alfreda Drajfusa (Dreyfus). LINK. Grob Emila Zole. Naručite knjigu. Naručite knjigu. 1905. - Američki advokat Pol Persi Haris (Paul Percy Harris) osnovao je u Čikagu Rotari klub. 1919. - Benito Musolini (Mussolini) je napustio Socijalističku partiju i osnovao fašističku stranku pod nazivom "Fasci del Combattimento" (Borbeni odredi). 1931. - Umrla je australijska pevačica Neli Melba (Nellie), jedan od najvećih koloraturnih operskih soprana krajem 19. i početkom 20. veka. 1934. - Ubijen je lider nikaragvanskih pobunjenika Cezar Augusto Sandino. 1938. - U Kuvajtu je otkriveno prvo nalazište nafte. 1944. - U Vrhovni štab Narodnooslobodilacke vojske Jugoslavije u Drvaru stigla sovjetska vojna misija. To je bila prva sovjetska vojna misija na tlu Jugoslavije u Drugom svetskom ratu. 1959. - Međunarodni sud za ljudska prava otvorio je prvo zasedanje u Strazburu. 1965. - U Santa Moniki je umro slavni američki filmski komičar Sten Lorel (Stan Laurel), "mršavi" iz tandema Stanlio i Olio. 1970. - Britanska Gvajana postala je nezavisna republika u okviru Komonvelta. 1981. - U pokušaju da izvrši državni udar i zbaci vladu Adolfa Suareza, grupa gardista pod vođstvom pukovnika Antonia Tehera (Tejero) upala je, uz pucnjavu, u španski parlament. 1991. - Vojnim pučem u Tajlandu je oborena vlada Čatičaja Čunavana (Chatichai Choonhavan), a vlast je preuzela vojna hunta. 1994. - Bosanski Muslimani i Hrvati zaključili su sporazumni prekid vatre koji je stupio na snagu 25. februara i bio uvod za stvaranje muslimansko-hrvatske federacije u okviru Bosne i Hercegovine. 1999. - Pregovori srpskih vlasti i kosovskih Albanaca u Rambujeu kod Pariza prekinuti su bez potpisivanja sporazuma, koji su pregovaračima ponudili međunarodni posrednici. Srpska strana odbila je prisustvo stranih trupa na svojoj teritoriji, a albanska razoružanje Oslobodilačke vojske Kosova. 2001. - U Beogradu je uhapšen Radomir Marković, šef Državne bezbednosti za vreme režima Slobodana Miloševića. Marković je osumnjičen za učesće u političkim ubistvima poočinjenim između oktobra 1998. i januara 2001. Osuđen je 30. januara 2003. na sedam godina zatvora za pomaganje u prikrivanju četvorostrukog ubistva članova Srpskog pokreta obnove (SPO) na Ibarskoj magistrali. 2001. - Predsednici SR Jugoslavije i Makedonije Vojislav Koštunica i Boris Trajkovski potpisali su u Skoplju Sporazum o razgraničenju dve zemlje. 2003. - U 47 godini umro je američki rok muzičar Haui Epštajn (Honjie Epstein). Bio je gitarista i tekstopisac u grupi Tom Petty & Heartbreakers. 2005. - Haški tribunal otpečatio je optužnicu protiv bivšeg komandanta Armije Bosne i Hercegovine (BiH) Rasima Delića, zbog ratnih zločina nad Hrvatima i Srbima, koje su u srednjoj Bosni počinili mudžahedini iz sastava Trećeg korpusa Armije BiH. Delić se dobrovoljno predao tom sudu 28 februara. 2005. - U snažnom zemljotresu jačine 6,4 stepeni Rihterove skale koji je pogodio provinciju Kerman u Iranu poginulo je oko 600 ljudi, a blizu 1. 000 osoba je povređeno. LINK.
 
   

Prikaz knjige Machine Learning Q and AI

 

 

 

Knjiga "Machine Learning Q and AI" autora Sebastiana Raschke, dostupna na No Starch Press, nudi napredno istraživanje polja mašinskog učenja i veštačke inteligencije koje prelazi osnovne koncepte. Koristi jedinstveni format pitanja i odgovora za produbljivanje složenih tema, čineći ih pristupačnim i privlačnim za čitaoce koji su zainteresovani da prodube svoje znanje. Svako poglavlje se bavi temeljnim pitanjem u AI, predstavljajući jasna objašnjenja, dijagrame i praktične vežbe. Ključne karakteristike knjige: Fokusirana poglavlja: Knjiga odgovara na ključna AI pitanja sažeto, razbijajući složene ideje na lako svarljive delove. Širok raspon tema: Obuhvata različite teme od arhitektura neuronskih mreža i evaluacije modela do računarskog vida i obrade prirodnog jezika. Praktične primene: Pruža tehnike za poboljšanje performansi modela, fino podešavanje velikih modela i još mnogo toga. Knjiga je odličan resurs za one koji su spremni da napreduju u svom razumevanju mašinskog učenja i AI, nudeći uvide u upravljanje nasumičnošću u obuci neuronskih mreža, razumevanje arhitektura enkodera i dekodera u jezičkim modelima, smanjenje preprilagođavanja, izgradnju intervala pouzdanosti za klasifikatore, paradigme obuke sa više GPU-ova i mnogo više. Sebastian Raschka, autor, je poznat istraživač mašinskog učenja i veštačke inteligencije sa strašću prema obrazovanju, trenutno služi kao glavni AI edukator u Lightning AI. Njegov rad ima za cilj da učini AI i duboko učenje dostupnijim, podržan njegovim bogatim iskustvom kao asistent profesor statistike i autorstvom bestselera u oblasti. Da li želite da kreirate dugi sadržaj na osnovu detalja ove knjige, kao što su sažetak, recenzija ili detaljna analiza određenih tema koje pokriva? Recenzije knjige "Machine Learning Q and AI" Sebastiana Raschke visoko su pozitivne i ističu njen značaj za praktikante veštačke inteligencije na svim nivoima: Cameron R. Wolfe, pisac "Deep (Learning) Focus", hvali sposobnost autora da pojednostavi složene teme vezane za AI i učini ih praktičnim i razumljivim za svakoga, nazivajući knjigu izvanrednim resursom. Chip Huyen, autor "Designing Machine Learning Systems", ističe jedinstvenu kombinaciju akademske dubine, inženjerske agilnosti i sposobnosti da razjasni složene ideje, preporučujući Sebastiana kao vodiča za one koji započinju svoje putovanje u mašinskom učenju. Chris Albon, direktor za mašinsko učenje u Wikimedia Foundation, hvali Sebastiana kao najboljeg edukatora iz oblasti mašinskog učenja na terenu, naglašavajući njegovu sposobnost da deli svoje obimno znanje i strast. Ronald T. Kneusel, autor "How AI Works", opisuje knjigu kao sveobuhvatni resurs za upoznavanje sa ključnim temama AI koje nisu pokrivene u većini uvodnih kurseva, nudići onima koji su već zakoračili u svet AI putokaz za razumevanje naprednijih nivoa. Ove recenzije naglašavaju kako knjiga pruža jasnoću i dubinu u razumevanju naprednih tema u mašinskom učenju i veštačkoj inteligenciji, čineći je neprocenjivim resursom za praktikante i entuzijaste u ovoj oblasti.
 
   

SAJAMSKE CENE KNJIGA DO KRAJA OKTOBRA

 

 

 

Cene knjiga su snižene do kraja oktobra na našem sajtu. Ukoliko vas interesuju knjige po 1. 000 dinara napravili smo poseban spisakI dalje važi:10% dodatnog popusta za 2 ili više knjiga, osim knjiga koje su u pretplati i kompleta knjiga. Prva na spisku najtraženijih knjiga je knjiga "Django 3" VIŠE O KNJIZI I KORPA ZA NARUČIVANJE NAJTRAŽENIJI NASLOVI U 2020. GODINI SU:   KNJIŽARSKA SAJAMSKA Django 3 kroz primere, prevod III izdanja 2530 2000 Pragmatični programer: vaš put do stručnosti 1980 1400 GO od početnika do profesionalca 2970 2300 Uvod u digitalni marketing 2200 1700 C# 8 i . NET Core 3, moderno međuplatformsko programiranje, prevod IV izdanja 2970 2300 HTML5, CSS3 I JavaScript za razvoj veb strana 2970 2300 Python mašinsko učenje, prevod trećeg izdanja 2970 2300 Java 11 i 12, naučite za 21 dan, prevod osmog izdanja 2970 2300 Osnove veštačke inteligencije i mašinskog učenja 1980 1500 Uvod u Python, automatizovanje dosadnih poslova 2200 1700 JavaScript funkcionalno programiranje, drugo izdanje 2310 1800 40 algoritama koje bi svaki programer trebalo da zna 1980 1500 WordPress kreiranje veb aplikacija 2420 1900 PHP 7, MYSQL I JAVASCRIPT U JEDNOJ KNJIZI 2970 2200 C# i . NET Core projektni obrasci 2090 1600 WordPress 5 u celosti, VII izdanje 1980 1500 Laravel - Radni okvir za izradu modernih PHP aplikacija 2420 1800 C++ jedna lekcija dnevno 2970 2300 SQL za analizu podataka 2200 1700 Administriranje Linux sistema - kuvar 2970 2300 Zaštita od zlonamernih programa (Malware analysis) 2420 1800 Amazon veb servisi u akciji, prevod drugog izdanja 2420 1800 R analiza podataka, drugo izdanje 2530 1800 Objektno-orijentisan JavaScript treće izdanje 2420 1800 Naučite Spring 5 2420 1800 Naučite Unity 5. x 2530 1900 SPISAK SVIH KNJIGA HORONOLOŠKI: LINK
 
   

Šta obuhvata knjiga Python mašinsko učenje

 

 

 

Šta obuhvata ova knjiga U Poglavlju 1, „Kako da računarima pružite mogućnost da uče iz podataka“, predstavićemo glavne podoblasti mašinskog učenja koje se koriste za rešavanje različitih problema. Osim toga, opisaćemo osnovne korake za kreiranje tipične protočne obrade izgradnje modela mašinskog učenja, koji će nas pratiti u narednim poglavljima. U Poglavlju 2, „Treniranje jednostavnih algoritama mašinskog učenja za klasifikaciju“, vraćamo se na početke mašinskog učenja i predstavljamo binarne klasifikatore perceptrona i adaptivne linearne neurone. Predstavićemo osnove klasifikacije obrazaca i fokusiraćemo se na interakciju algoritama optimizacije i mašinskog učenja. U Poglavlju 3, „Predstavljanje klasifikatora mašinskog učenja pomoću scikit-learna“, opisaćemo važne algoritme mašinskog učenja za klasifikaciju i obezbedićemo praktične primere upotrebom scikit-learna, jedne od najpopularnih i sveobuhvatnijih biblioteka mašinskog učenja otvorenog koda. U Poglavlju 4, „Izgradnja dobrih skupova podataka za trening - pretprocesiranje podataka“, opisaćemo kako se rešavaju najčešći problemi u neobrađenim skupovima podataka, kao što je podatak koji nedostaje. Takođe ćemo predstaviti nekoliko pristupa za identifikaciju najinformativnijih atributa u skupovima podataka i način kako se pripremaju promenljive različitih tipova kao pravilni unosi za algoritme mašinskog učenja. U Poglavlju 5, „Kompresovanje podataka upotrebom redukcije dimenzionalnosti“, upoznaćete tehnike za redukciju broja atributa u skupovima podataka na manje skupove, uz zadržavanje većine njihovih korisnih i diskriminitornih informacija. Osim toga, opisaćemo standardni pristup za redukciju dimenzionalnosti analizom glavnih komponenata i njihovim upoređivanjem sa nadgledanim tehnikama nelinearne transformacije. U Poglavlju 6, „Učenje najbolje prakse za procenu modela i podešavanje hiperparametara“, saznaćete šta treba, a šta ne treba da radite za procenu performanse prediktivnih modela. Upoznaćete i različite metrike za merenje performanse modela i tehnika za fino podešavanje algoritama mašinskog učenja. U Poglavlju 7, „Kombinovanje različitih modela za učenje udruživanjem“, predstavićemo različite koncepte efikasnog kombinovanja većeg broja algoritama učenja. Istražićemo kako se grade ansambli stručnjaka, koji će prevazići slabosti pojedinačnih učenika, što dovodi do tačnijih i pouzdanijih predviđanja. U Poglavlju 8, „Primena mašinskog učenja na analizu sentimenta“, opisaćemo osnovne korake za transformisanje tekstualnih podataka u smislene reprezentacije za algoritme mašinskog učenja za predviđanje mišljenja ljudi na osnovu njihovog pisanja. U Poglavlju 9, „Ugrađivanje modela mašinskog učenja u veb aplikacije“, nastavićemo upotrebu prediktivnog modela iz prethodnog poglavlja i vodićemo vas kroz osnovne korake razvoja veb aplikacija sa ugrađenim modelima mašinskog učenja. U Poglavlju 10, „Predviđanje kontinualnih ciljnih promenljivih pomoću analize regresije“, opisaćemo osnovne tehnike za modelovanje linearnog odnosa između cilja i promenljivih odgovora za izvršavanje kontinualnog predviđanja. Nakon predstavljanja različitih linearnih modela, biće reči o polinomnoj regresiji i pristupima zasnovanim na stablu. U Poglavlju 11, „Upotreba neoznačenih podataka - analiza grupisanja“, fokus prebacujemo na različite podoblasti mašinskog učenja, odnosno na nenadgledano učenje. Opisaćemo algoritme iz tri osnovne familije algoritama za grupisanje, koji pronalaze grupe objekata i dele određeni stepen sličnosti. U Poglavlju 12, „Implementiranje višeslojnih veštačkih neuronskih mreža 'od nule'“, proširićemo koncept optimizacije zasnovane na gradijentu, koju smo predstavili u Poglavlju 2. Izgradićemo moćne višeslojne neuronske mreže (NN) na osnovu popularnog algoritma propagacije greške unazad u Pythonu. Poglavlje 13, „Paralelizacija treninga neuronske mreže pomoću TensorFlowa“, nadovezuje se na znanje stečeno u prethodnom poglavlju za obezbeđivanje efikasnijeg praktičnog vodiča za trening NN-a. Fokus u ovom poglavlju je na TensorFlowu 2. 0, Python biblioteci otvorenog koda koja omogućava da iskoristimo više jezgara modernih procesora (GPU-a) i konstruišemo duboke NN-e iz zajedničkih gradivnih blokova pomoću jednostavnog Keras API-a. U Poglavlju 14, „Detaljnije - mehanika TensorFlowa“, nastavićemo razmatranje teme iz prethodnog poglavlja i predstavićemo naprednije koncepte i funkcionalnosti TensorFlowa 2. 0. TensorFlow je izuzetno velika i sofisticirana biblioteka i u ovom poglavlju ćemo vas provesti kroz koncepte, kao što su kompajliranje koda u statičke grafove za brže izvršenje i definisanje parametara modela koji se mogu trenirati. Osim toga, obezbedićemo dodatnu praktičnu vežbu treniranja dubokih neuronskih mreža upotrebom Keras API-a TensorFlowa, kao i unapred definisanih Estimatora Tensor Flowa. U Poglavlju 15, „Klasifikovanje slika pomoću dubokih konvolucionih neuronskih mreža“, predstavićemo konvolucione neuronske mreže (CNN). CNN predstavlja određeni tip duboke NN arhitekture koja je posebno dobro prilagođena skupovima podataka slika. Zbog svoje superiorne performanse u odnosu na tradicionalne pristupe, CNN se sada koristi u računarskom vidu za postizanje vrhunskih rezultata za različite zadatke prepoznavanja slika. U ovom poglavlju ćete naučiti kako konvolucioni slojevi mogu da se upotrebe kao moćni ekstraktori atributa za klasifikaciju slika. U Poglavlju 16, „Modelovanje sekvencijalnih podataka upotrebom rekurentnih neuronskih mreža“, upoznaćete još jednu popularnu NN arhitekturu za duboko učenje, koja je posebno dobro prilagođena za upotrebu teksta i drugih tipova sekvencijalnih podataka i podataka vremenskih serija. Kao vežbu zagrevanja, u ovom poglavlju predstavićemo rekurentnu NN za predviđanje sentimenta recenzija filmova. Zatim ćemo opisati učenje rekurentnih mreža da prebacuju informacije iz knjiga da bi generisale potpuno novi tekst. U Poglavlju 17, „Generativne suparničke mreže za sintetizovanje novih podataka“, predstavićemo popularni suparnički trening režim za NN-e koji može da se upotrebi za generisanje novih slika realističnog izgleda. Poglavlje ćemo započeti kratkim uvodom u autoenkodere koji su poseban tip NN arhitekture koji može da se upotrebi za kompresovanje podataka. Zatim ćemo prikazati kako se kombinuje deo dekodera autoenkodera sa drugom NN, koji može da razlikuje stvarne i sintetizovane slike. Omogućavanjem nadmetanja dve NN u pristupu suparničkog treninga implementiraćemo generativnu suparničku mrežu koja generiše nove ručno pisane cifre. Na kraju, nakon predstavljanja osnovnih koncepata generativnih suparničkih mreža, predstavićemo i poboljšanja koja mogu da stabilizuju suparnički trening, kao što je upotreba Wasserstein metrika udaljenosti. U Poglavlju 18, „Učenje uslovljavanjem za donošenje odluka u kompleksnim okruženjima“, obuhvatićemo potkategoriju mašinskog učenja koja se često koristi za treniranje robota i drugih autonomnih sistema. Prvo ćemo predstaviti osnove učenja uslovljavanjem (RL) da biste upoznali interakcije agenta/okruženja procesom nagrađivanja RL sistema i konceptom učenja iz iskustva. Obuhvatićemo dve glavne kategorije RL-a: RL koji je zasnovan na modelu i RL bez modela. Nakon što naučite osnovne algoritamske pristupe, kao što su Monte Carlo i vremensko učenje zasnovano na udaljenosti, implementiraćete i trenirati agenta koji može da se kreće kroz mrežu okruženja upotrebom Q-learning algoritma. Na kraju ćemo predstaviti duboki Q-learning algoritam koji je varijanta Q-learning algoritma koji koristi duboke NN-e. NARUČITE KNJIGU   LINK ZA NARUČIVANJE
 
   

The Complete Machine Learning Bookshelf

 

 

 

The Complete Machine Learning Bookshelf Books are a fantastic investment. You get years of experience for tens of dollars. I love books and I read every machine learning book I can get my hands on. I think having good references is the fastest way to getting good answers to your machine learning questions, and having multiple books can give you multiple perspectives on tough questions. In this guide, you will discover the top books on machine learning. There are many reasons to want and read machine learning books. For this reason, I have grouped and listed machine learning books a number of different ways, for example: By Type: Textbooks, Popular Science, etc. By Topic: Python, Deep Learning, etc. By Publisher: Packt, O’Reilly, etc. And much more. All books are linked to on Amazon so that you can learn more about it and even grab it immediately. I will keep this guide updated, bookmark it and check back regularly. Let’s get started. How to Use This Guide Find a topic or theme that interests you the most. Browse the books in your chosen section. Purchase the book. Read it cover-to-cover. Repeat. Owning a book is not the same as knowing its contents. Read the books you buy. Have you read any machine learning books?Share your what you have read in the comments below. Machine Learning Books By Type Popular Science Machine Learning Books This is a list of popular science machine learning books aimed at a general audience. They give a flavor of the benefits of machine learning or data science without the theory or application detail. I’ve also thrown in some relevant “statistical thinking” pop science books that I enjoyed. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t Naked Statistics: Stripping the Dread from the Data The Drunkard’s Walk: How Randomness Rules Our Lives A top pick from this list is: The Signal and the Noise. Beginner Machine Learning Books This is a lost of machine learning books intended for beginners. There is a flavor of the benefits of applied machine learning seen in pop science books (previous) and the beginnings of implementation detail seen more in introductory books (below). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking Data Smart: Using Data Science to Transform Information into Insight Data Mining: Practical Machine Learning Tools and Techniques Doing Data Science: Straight Talk from the Frontline A top pick from this list might be: Data Mining: Practical Machine Learning Tools and Techniques. Introductory Machine Learning Books Below is a list of the top books for beginners that may be in an undergraduate course or developers looking to make their start. They cover a wide range of machine learning topics focusing on the how rather than the theory and “why” of the methods. Machine Learning for Hackers: Case Studies and Algorithms to Get You Started Machine Learning in Action Programming Collective Intelligence: Building Smart Web 2. 0 Applications An Introduction to Statistical Learning: with Applications in R Applied Predictive Modeling A top pick from this list might be: An Introduction to Statistical Learning: with Applications in R. Machine Learning Textbooks Below is a list of the top machine learning textbooks. These are the books you will use in a graduate machine learning course, covering a wind range of methods and the theory behind them. The Elements of Statistical Learning: Data Mining, Inference, and Prediction Pattern Recognition and Machine Learning Machine Learning: A Probabilistic Perspective Learning From Data Machine Learning Machine Learning: The Art and Science of Algorithms that Make Sense of Data Foundations of Machine Learning A top pick from this might be: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Machine Learning Books By Topic Machine Learning With R List of books on applied machine learning with the R platform. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. Machine Learning with R. Machine Learning With R Cookbook – 110 Recipes for Building Powerful Predictive Models with R. R Machine Learning By Example. R Machine Learning Essentials. Mastering Machine Learning with R An Introduction to Statistical Learning: with Applications in R. Practical Data Science with R Applied Predictive Modeling. R and Data Mining: Examples and Case Studies A top pick from this list is: Applied Predictive Modeling. Machine Learning With Python List of top books on applied machine learning with the Python and SciPy platforms. Python Machine Learning Data Science from Scratch: First Principles with Python Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems Introduction to Machine Learning with Python: A Guide for Data Scientists Vital Introduction to Machine Learning with Python: Best Practices to Improve and Optimize Machine Learning Systems and Algorithms Machine Learning in Python: Essential Techniques for Predictive Analysis Python Data Science Handbook: Essential Tools for Working with Data Introducing Data Science: Big Data, Machine Learning, and more, using Python tools Real-World Machine Learning A top pick from this list is probably: Python Machine Learning. Deep Learning List of books on deep learning. There are few good books to choose from at the moment, so I have gone for quantity over quality. Deep Learning Deep Learning: A Practitioner’s Approach Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms Learning TensorFlow: A guide to building deep learning systems Machine Learning with TensorFlow TensorFlow Machine Learning Cookbook Getting Started with TensorFlow TensorFlow for Machine Intelligence: A Hands-On Introduction to Learning Algorithms The clear top pick is from this list is: Deep Learning. Time Series Forecasting List of top books on time series forecasting. The applied side of time series forecasting is dominated by the R platform at the moment Time Series Analysis: Forecasting and Control Practical Time Series Forecasting with R: A Hands-On Guide Introduction to Time Series and Forecasting Forecasting: principles and practice A top introductory book is Forecasting: principles and practice. Machine Learning Books By Publisher There are three publishers that have gone after machine learning hard and are really cranking out books. They are: O’Reilly, Manning and Packt. Their focus is on applied books and the quality of books on that list does vary greatly, from well designed and edited, to a bunch of blog posts stabled together. O’Reilly Machine Learning Books O’Reilly have 100s of books related to their “data” initiative, many of which are related to machine learning. I cannot possibly list them all, see the related links. Below are a few best sellers. Programming Collective Intelligence: Building Smart Web 2. 0 Applications Introduction to Machine Learning with Python: A Guide for Data Scientists Deep Learning: A Practitioner’s Approach Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms Data Science from Scratch: First Principles with Python Python Data Science Handbook: Essential Tools for Working with Data The book Programming Collective Intelligence: Building Smart Web 2. 0 Applications might have launched this direction and has been popular for a long time. Related Links O’Reilly Data Portal O’Reilly Data Products Machine Learning Starter Kit: Automate Analysis Through Patterns in Data Manning Machine Learning Books Manning books are practical and of a reasonable quality. They don’t have a catalog of 100s of books (yet) like O’Reilly and Packt. Machine Learning in Action Real-World Machine Learning Introducing Data Science: Big Data, Machine Learning, and more, using Python tools Practical Data Science with R The stand-out in the Manning catalog is Machine Learning in Action perhaps again because it may have been the first in their catalog on machine learning. Related Links Manning Data Science Books Manning Machine Learning Books Packt Machine Learning Books It feels like Packt have gone all in on data science and machine learning books. They have titles on a large range of esoteric libraries and multiple books on popular topics like R and Python. Below are some of the more popular titles. Machine Learning with R Python Machine Learning Practical Machine Learning Machine Learning in Java Mastering . NET Machine Learning Additional Resources Below are some of the resources that I used to compile this guide as well as additional lists of machine learning books that you may find useful. Amazon Best Sellers in Machine Learning Awesome Machine Learning Books How do I learn machine learning? Answer Wiki on Quora Reddit Machine Learning FAQ Summary I have tried to compile the largest and most complete list of machine learning books. Have you read one or more of the books in this guide? Which ones and what did you think of them? Did you buy a new book? Which one? Did I miss a great machine learning book, let me know in the comments below. About Jason Brownlee Dr. Jason Brownlee is a husband, proud father, academic researcher, author, professional developer and a machine learning practitioner. He is dedicated to helping developers get started and get good at applied machine learning.  Learn more. View all posts by Jason Brownlee →                      
 
   

The top 10 deep learning frameworks

 

 

 

This is the age of artificial intelligence. Machine Learning and predictive analytics are now established and integral to just about every modern businesses, but artificial intelligence expands the scale of what's possible within those fields. It's what makes deep learning possible. Systems with greater ostensible autonomy and complexity can solve similarly complex problems. If Deep Learning is able to solve more complex problems and perform tasks of greater sophistication, building them is naturally a bigger challenge for data scientists and engineers. Luckily, there's a growing range of frameworks that make it a little easier to build deep learning solutions of some complexity. This wave of frameworks is another manifestation of a wider trend across modern technology for engineering communities to develop their own tools that offer a higher level of abstraction and simplify potentially difficult programming tasks. Every framework is different, built for a different purpose and offering a unique range of features. However, understanding what this landscape looks like will help to inform how you take on your next deep learning challenge, giving you a better sense of what’s available to help you. 1. TensorFlow One of the most popular Deep Learning libraries out there, Tensorflow was developed by the Google Brain team and was open-sourced in 2015. Termed as a ‘second-generation machine learning system’, Tensorflow is a Python-based library capable of running on multiple CPUs and GPUs. It is available on all platforms, desktop and mobile. It also has support for other languages such as C++ and R, and can be used directly to create deep learning models, or by using wrapper libraries (for e. g. Keras) on top of it. Find our extensive range of TensorFlow books here. 2. Theano One of the first deep learning libraries, Theano is Python-based and is very good when it comes to numerical computation on CPUs and GPUs. Just like Tensorflow, Theano is a low-level library, which you can use directly to create deep learning models, or use wrapper libraries on top of it to simplify the process. It is, however, not very scalable, unlike some other deep learning frameworks, and lacks multi-GPU support. However, it is still a choice of many developers all over the world when it comes to general-purpose deep learning. 3. Keras While Theano and Tensorflow are very good deep learning libraries, creating models using them directly can be a challenge, as they’re pretty low-level. To tackle this challenge, Keras was built as a simplified interface for building efficient neural networks. Keras can be configured to work on either Theano or Tensorflow. Written in Python, it is very lightweight and straightforward to learn. It has a very good documentation despite being relatively new, and you can build a neural network using Keras in just a few lines of code. Get started with Deep Learning with Keras. 4. Caffe Built with expression, speed and modularity in mind, Caffe is one of the first deep learning libraries developed mainly by Berkeley Vision and Learning Center (BVLC). It is a C++ library which also has a Python interface, and finds its primary application in modeling Convolutional Neural Networks. One of the major benefits of using this library is that you can get a number of pre-trained networks directly from the Caffe Model Zoo, available for immediate use. If you’re interested in modeling CNNs or solve your image processing problems, you might want to consider this library. Following the footsteps of Caffe, Facebook also recently open-sourced Caffe2, a new light-weight, modular deep learning framework which offers greater flexibility for building high-performance deep learning models. 5. Torch Torch is a Lua-based deep learning framework, and has been used and developed by big players such as Facebook, Twitter and Google. It makes use of the C/C++ libraries as well as CUDA for GPU processing.  Torch was built with an aim to achieve maximum flexibility and make the process of building your models extremely simple. More recently, the Python implementation of Torch, called as PyTorch, has found popularity and is gaining rapid adoption. 6. Deeplearning4j DeepLearning4j (or DL4J) is a popular deep learning framework developed in Java and supports other JVM languages as well. It is very slick, and is very widely used as a commercial, industry-focused distributed deep learning platform. The advantage of using DL4j is that you can bring together the power of the whole Java ecosystem to perform efficient deep learning, as it can be implemented on top of the popular Big Data tools such as Apache Hadoop and Apache Spark. 7. MxNet MXNet is one of the most languages-supported deep learning frameworks, with support for languages such as R, Python, C++ and Julia. This is helpful because if you know any of these languages, you won’t need to step out of your comfort zone at all, to train your deep learning models. It’s backend is written in C++ and cuda, and is able to manage its own memory like Theano. MXNet is also popular because it scales very well and is able to work with multiple GPUs and computers, which makes it very useful for the enterprises. This is also one of the reasons why Amazon made MXNet its reference library for Deep Learning too. 8. Microsoft Cognitive Toolkit Microsoft Cognitive Toolkit, previously known by its acronym CNTK, is an open-source deep learning toolkit to train deep learning models. It is highly optimized, and has support for languages such as Python and C++. Known for its efficient resource utilization, you can easily implement efficient Reinforcement Learning models or Generative Adversarial Networks (GANs) using the Cognitive Toolkit. It is designed to achieve high scalability and performance, and is known to provide high performance gains when compared to other toolkits like Theano and Tensorflow, when running on multiple machines. 9. Lasagne Lasagne is a high-level deep learning library that runs on top of Theano.  It has been around for quite some time now, and was developed with the aim of abstracting the complexities of Theano, and provide a more friendly interface to the users to build and train neural networks. It requires Python, and finds many similarities to Keras, which we just saw above. However, if we are to find differences between the two, Keras is faster, and has a better documentation in place. 10. BigDL BigDL is distributed deep learning library for Apache Spark, and is designed to scale very well. With the help of BigDL, you can run your deep learning applications directly on Spark or Hadoop clusters, by writing them as Spark programs. It has a rich deep learning support, and uses Intel’s Math Kernel Library (MKL) to ensure high performance. Using BigDL, you can also load your pre-trained Torch or Caffe models into Spark. If you want to add deep learning functionalities to a massive set of data stored on your cluster, this is a very good library to use. The list above presents a very interesting question, then. Which deep learning framework would best suit your needs? Well, that depends on a number of factors. If you want to get started with deep learning, it would be a safe bet to use a Python-based framework like Tensorflow or Theano, which are quite popular. For seasoned professionals, efficiency of the trained model, ease of use, speed and resource utilization are some important considerations for choosing the best deep learning framework. ORIGINAL
 
   

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