Best deep learning books to examine the foundations of machine learning and neural networks
Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. Its goal is to learn complex functions using special neural network architectures that are "deep" (consist of many layers).
Deep learning (also known as deep structured learning or hierarchical learning) is a part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.read more To help you learn and work with deep learning you should read books and here we will suggest you some best deep learning books.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.
What You Will Learn
- Explore the machine learning landscape, particularly neural nets
- Use scikit-learn to track an example machine-learning project end-to-end
- Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
- Use the TensorFlow library to build and train neural nets
- Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
- Learn techniques for training and scaling deep neural nets
- Apply practical code examples without acquiring excessive machine learning theory or algorithm details
"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, co-chair of OpenAI; co-founder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs.
It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and video games.
Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp.
What You Will Learn
- Examine the foundations of machine learning and neural networks
- Learn how to train feed-forward neural networks
- Use TensorFlow to implement your first neural network
- Manage problems that arise as you begin to make networks deeper
- Build neural networks that analyze complex images
- Perform effective dimensionality reduction using autoencoders
- Dive deep into sequence analysis to examine the language
- Understand the fundamentals of reinforcement learning
Neural networks have been a mainstay of artificial intelligence since its earliest days. Now, exciting new technologies such as deep learning and convolution are taking neural networks in bold new directions.
In this book, we will demonstrate the neural networks in a variety of real-world tasks such as image recognition and data science. We examine current neural network technologies, including ReLU activation, stochastic gradient descent, cross-entropy, regularization, dropout, and visualization.
Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics.
What You Will Learn
- Get up and running with TensorFlow, rapidly and painlessly
- Learn how to use TensorFlow to build deep learning models from the ground up
- Train popular deep learning models for computer vision and NLP
- Use extensive abstraction libraries to make development easier and faster
- Learn how to scale TensorFlow, and use clusters to distribute model training
- Deploy TensorFlow in a production setting
Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject but also helps you get started building efficient deep learning networks.
What You Will Learn
- Dive into machine learning concepts in general, as well as deep learning in particular
- Understand how deep networks evolved from neural network fundamentals
- Explore the major deep network architectures, including Convolutional and Recurrent
- Learn how to map specific deep networks to the right problem
- Walk through the fundamentals of tuning general neural networks and specific deep network architectures
- Use vectorization techniques for different data types with DataVec, DL4J’s workflow tool
- Learn how to use DL4J natively on Spark and Hadoop
The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning.
What You'll Learn- Understand the intuition and mathematics that power deep learning models
- Utilize various algorithms using the R programming language and its packages
- Use best practices for experimental design and variable selection
- Practice the methodology to approach and effectively solve problems as a data scientist
- Evaluate the effectiveness of algorithmic solutions and enhance their predictive power.
About This Book
- Learn Theano basics and evaluate your mathematical expressions faster and in an efficient manner
- Learn the design patterns of deep neural architectures to build efficient and powerful networks on your datasets
- Apply your knowledge to concrete fields such as image classification, object detection, chatbots, machine translation, reinforcement agents, or generative models.
What You Will Learn
- Get familiar with Theano and deep learning
- Provide examples in supervised, unsupervised, generative, or reinforcement learning.
- Discover the main principles for designing efficient deep learning nets: convolutions, residual connections, and recurrent connections.
- Use Theano on real-world computer vision datasets, such as for digit classification and image classification.
- Extend the use of Theano to natural language processing tasks, for chatbots or machine translation
- Cover artificial intelligence-driven strategies to enable a robot to solve games or learn from an environment
- Generate synthetic data that looks real with generative modeling
- Become familiar with Lasagne and Keras, two frameworks built on top of Theano
The advent of deep learning has been transformative for many difficult problems in machine learning, often delivering breakthrough performance compared with previous techniques. This paradigm shift has swept the field of natural language processing, where an emerging deep-learning approach has set the state of the art in text categorization, information extraction, recommendations, and more.
Deep Learning with Text is the first practitioner’s guide to deep learning with text. With it, you’ll learn how to use "batteries-included" libraries in Python—including spaCy, genism, and Keras—to apply this modern, deep-learning approach to solve real-world problems with natural language text.
Until now, much of the published material about deep learning has been sequestered in research papers and graduate-level academic textbooks. This practical book will enable software developers and data scientists to build new products and systems that have only become possible in the past couple of years.
Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems.
This text serves as a primer for computer architects in a new and rapidly evolving field. We review how machine learning has evolved since its inception in the 1960s and track the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade.
The remainder of the book is dedicated to the design and optimization of hardware and architectures for machine learning. As high-performance hardware was so instrumental in the success of machine learning becoming a practical solution, this chapter recounts a variety of optimizations proposed recently to further improve future designs.
Key Features
- Develop your skills to implement advance techniques in deep learning using Google's Tensorflow 1.x
- Implement real-world and practical examples to illustrate deep learning techniques.
- Hands-on recipes to learn how to design and train a multi-layer neural network with TensorFlow 1.x
What you will learn
- Install Tensorflow and use it for CPU and GPU options.
- Implement DNNs and apply the knowledge to solve different AI-driven problems.
- Use Tensorflow to implement DNNs and apply the knowledge to solve different AI-driven problems.
- Peek into different data sets available with the Tensorflow, how to access them and use them in your code.
- Learn the use of Tensorboard to understand the architecture, optimize the learning process and peek inside the neural network black box.
- Use different regression techniques for the task of prediction and classification. You will apply them for predicting house prices and identification of handwritten digits.
- Implement single and multilayer Perceptrons in Tensorflow and use them for the identification of handwritten digits
- Implement CNN in Tensorflow, and use it to classify CIFAR-10 images.
- Process images and use CNN to differentiate between cats and Dogs.
- Understand RNN and implement it to perform the task of text generation.
- Learn about restricted Boltzmann Machines, implement them in Tensorflow and use it for recommending movies.
- Understand the implementation of Autoencoders, and deep belief networks, use them for emotion detection.
- Different Reinforcement Learning methods and their implementation. Use them for making a game playing agent.
- GANs and its implementation in Tensorflow
About This Book
- Develop a sound strategy to solve predictive modelling problems using the most popular machine learning Java libraries.
- Explore a broad variety of data processing, machine learning, and natural language processing through diagrams, source code, and real-world applications
- This step-by-step guide will help you solve real-world problems and links neural network theory to their application
What You Will Learn
- Get a practical deep dive into machine learning and deep learning algorithms
- Explore neural networks using some of the most popular Deep Learning frameworks
- Dive into Deep Belief Nets and Stacked Denoising Autoencoders algorithms
- Apply machine learning to fraud, anomaly, and outlier detection
- Experiment with deep learning concepts, algorithms, and the toolbox for deep learning
- Select and split data sets into training, test, and validation, and explore validation strategies
- Apply the code generated in practical examples, including weather forecasting and pattern recognition.
About This Book
- Master intricacies of R deep learning packages such as mxnet & tensorflow
- Learn application on deep learning in different domains using practical examples from text, image and speech
- Guide to set-up deep learning models using CPU and GPU
What You Will Learn
- Build deep learning models in different application areas using TensorFlow, H2O, and MXnet.
- Analyzing a Deep boltzmann machine
- Setting up and Analysing Deep belief networks
- Building supervised model using various machine learning algorithms
- Set up variants of basic convolution function
- Represent data using Autoencoders.
- Explore generative models available in Deep Learning.
- Discover sequence modeling using Recurrent nets
- Learn fundamentals of Reinforcement Leaning
- Learn the steps involved in applying Deep Learning in text mining
- Explore application of deep learning in signal processing
- Utilize Transfer learning for utilizing pre-trained model
- Train a deep learning model on a GPU.
The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormus profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go.
Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data.
Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain.
