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Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with Python, 2nd Edition
Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with Python, 2nd Edition

Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow.

Key Features

  • Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow
  • Explore deep neural...
Hands-On Automated Machine Learning: A beginner's guide to building automated machine learning systems using AutoML and Python
Hands-On Automated Machine Learning: A beginner's guide to building automated machine learning systems using AutoML and Python

Automate data and model pipelines for faster machine learning applications

Key Features

  • Build automated modules for different machine learning components
  • Understand each component of a machine learning pipeline in depth
  • Learn to use different open source...
Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with Python
Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with Python

Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras.

The overall...

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource...

Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras
Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras

Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks

Key Features

  • Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision
  • Combine the power of Python, Keras, and TensorFlow to...
Reinforcement Learning with TensorFlow: A beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym
Reinforcement Learning with TensorFlow: A beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym

Leverage the power of reinforcement learning techniques to develop self-learning systems using TensorFlow

Key Features

  • Explore reinforcement learning concepts and their implementation using TensorFlow
  • Discover different problem-solving methods for reinforcement...
Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning
Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or...

Practical Convolutional Neural Networks: Implement advanced deep learning models using Python
Practical Convolutional Neural Networks: Implement advanced deep learning models using Python

One stop guide to implementing award-winning, and cutting-edge CNN architectures

Key Features

  • Fast-paced guide with use cases and real-world examples to get well versed with CNN techniques
  • Implement CNN models on image classification, transfer learning, Object Detection,...
TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning
TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning

Learn how to solve challenging machine learning problems with TensorFlow, Google’s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects...

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Graphics in this book are printed in black and white.

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....

Deep Belief Nets in C++ and CUDA C: Volume 1: Restricted Boltzmann Machines and Supervised Feedforward Networks
Deep Belief Nets in C++ and CUDA C: Volume 1: Restricted Boltzmann Machines and Supervised Feedforward Networks
Discover the essential building blocks of the most common forms of deep belief networks. At each step this book provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers...
Apache Spark Deep Learning Cookbook: Over 80 recipes that streamline deep learning in a distributed environment with Apache Spark
Apache Spark Deep Learning Cookbook: Over 80 recipes that streamline deep learning in a distributed environment with Apache Spark

A solution-based guide to put your deep learning models into production with the power of Apache Spark

Key Features

  • Discover practical recipes for distributed deep learning with Apache Spark
  • Learn to use libraries such as Keras and TensorFlow
  • Solve...
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