Deeply learn from these Deep Learning Books

By ridhigrg |Email | Apr 27, 2020 | 1323 Views

Deep Learning (Adaptive Computation and Machine Learning series) Hardcover - 3 January 2017by Ian Goodfellow
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. "Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -Elon Musk, cochair of OpenAI; cofounder 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. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. 
This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. 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 videogames.

Deep Learning: A Practitioner's Approach Paperback- 1 January 2017by Josh Patterson 
Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far.
Through real-world examples, you'­ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.

  • 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
  • Walkthrough 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

Deep Learning with R 1st Edition
by Francois Chollet
Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher Fransois Chollet and adapted for R by RStudio founder J. J. Allaire, this book builds your understanding of deep learning through intuitive explanations and practical examples. You'll practice your new skills with R-based applications in computer vision, natural-language processing, and generative models.

What's Inside
  • Deep learning from first principles
  • Setting up your own deep-learning environment
  • Image classification and generation
  • Deep learning for text and sequences

Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks Kindle Edition
by Umberto Michelucci
Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You'll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function. 

What You Will Learn
  • Implement advanced techniques in the right way in Python and TensorFlow
  • Debug and optimize advanced methods (such as dropout and regularization)
  • Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on)
  • Set up a machine learning project focused on deep learning on a complex dataset

Introduction to Deep Learning (The MIT Press) Hardcover- January 29, 2019
by Eugene Charniak
This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework.

The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference.

Source: HOB