Some directly approachable Books of Deep Learning

Deep learning Machine Learning Books Software Engineer Algorithms Applications Python TensorFlow NLP Computer Science Neural Networks Artificial Intelligence Keras Google

Some directly approachable Books of Deep Learning

By ridhigrg |Email | Jan 21, 2020 | 1509 Views

Deep Learning (Adaptive Computation and Machine Learning series) Hardcover - 3 Jan 2017

by 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.

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.

Hands-On Deep Learning Algorithms with Python: Master deep learning algorithms with extensive math by implementing them using TensorFlow Kindle Edition

by Sudharsan Ravichandran

Understand basic to advanced deep learning algorithms, the mathematical principles behind them, and their practical applications.

Key Features

Get up-to-speed with building your own neural networks from scratch
Gain insights into the mathematical principles behind deep learning algorithms
Implement popular deep learning algorithms such as CNNs, RNNs, and more using TensorFlow

This book introduces you to popular deep learning algorithms-from basic to advanced-and shows you how to implement them from scratch using TensorFlow. Throughout the book, you will gain insights into each algorithm, the mathematical principles behind it, and how to implement it in the best possible manner. The book starts by explaining how you can build your own neural networks, followed by introducing you to TensorFlow, the powerful Python-based library for machine learning and deep learning.

Introduction to Deep Learning (The MIT Press) [Print Replica] Kindle Edition

by Eugene Charniak

A project-based guide to the basics of deep learning.

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.

An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language. Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python. The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference.

Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence (Undergraduate Topics in Computer Science) Kindle Edition

by Sandro Skansi

This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models that represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks, and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website.

Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism.

Deep Learning with Python Paperback - 22 Dec 2017

by Franaois Chollet

About the Book

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher Franaois Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.

What's Inside

Deep learning from first principles
Setting up your own deep-learning environment
Image-classification models
Deep learning for text and sequences
Neural style transfer, text generation, and image generation

Source: HOB

Share This On

Previous Story Next Story

Latest News Feed