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Ukupno: 6, 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.  
 
   

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
 
   

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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