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Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow, 2nd Edition
Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow, 2nd Edition

Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries

Key Features

  • Build a strong foundation in neural networks and deep learning with Python libraries
  • Explore advanced deep learning techniques and their applications...
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks

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...

Artificial Intelligence for Big Data: Complete guide to automating Big Data solutions using Artificial Intelligence techniques
Artificial Intelligence for Big Data: Complete guide to automating Big Data solutions using Artificial Intelligence techniques

Build next-generation artificial intelligence systems with Java

Key Features

  • Implement AI techniques to build smart applications using Deeplearning4j
  • Perform big data analytics to derive quality insights using Spark MLlib
  • Create self-learning systems using...
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 Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow
Hands-On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow

A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python

Key Features

  • Enter the world of artificial intelligence using the power of Python
  • An example-rich guide to master various RL and DRL algorithms
  • Explore...
Probability (Graduate Studies in Mathematics)
Probability (Graduate Studies in Mathematics)

This is a textbook for a one-semester graduate course in measure-theoretic probability theory, but with ample material to cover an ordinary year-long course at a more leisurely pace. Khoshnevisan's approach is to develop the ideas that are absolutely central to modern probability theory, and to showcase them by presenting their various...

Probability
Probability

This textbook is ideal for an undergraduate introduction to probability, with a calculus prerequisite. It is based on a course that the author has taught many times at Berkeley. The text's overall style is informal, but all results are stated precisely, and most are proved. Understanding is developed through intuitive explanations and...

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...

A Computational Introduction to Number Theory and Algebra
A Computational Introduction to Number Theory and Algebra
"This is an outstanding and well-written book whose aim is to introduce the reader to a broad range of material -- ranging from basic to relatively advanced -- without requiring any prior knowledge on the part of the reader other than calculus and mathematical maturity. That the book succeeds at this goal is quite an accomplishment! ...this...
Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models
Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models

This book was written in response to the growing demand for a text that provides a unified treatment of linear and nonlinear complex valued adaptive filters, and methods for the processing of general complex signals (circular and noncircular). It brings together adaptive filtering algorithms for feedforward (transversal) and feedback...

Mathematics of Discrete Structures for Computer Science
Mathematics of Discrete Structures for Computer Science

Mathematics plays a key role in computer science, some researchers would consider computers as nothing but the physical embodiment of mathematical systems. And whether you are designing a digital circuit, a computer program or a new programming language, you need mathematics to be able to reason about the design -- its correctness,...

Introduction to Probability and Statistics Using R
Introduction to Probability and Statistics Using R
This is a textbook for an undergraduate course in probability and statistics. The approximate prerequisites are two or three... More > semesters of calculus and some linear algebra. Students attending the class include mathematics, engineering, and computer science majors....
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