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Domain-Specific Languages in R: Advanced Statistical Programming
Domain-Specific Languages in R: Advanced Statistical Programming

Gain an accelerated introduction to domain-specific languages in R, including coverage of regular expressions. This compact, in-depth book shows you how DSLs are programming languages specialized for a particular purpose, as opposed to general purpose programming languages. Along the way, you’ll learn to specify tasks...

Keras Reinforcement Learning Projects: 9 projects exploring popular reinforcement learning techniques to build self-learning agents
Keras Reinforcement Learning Projects: 9 projects exploring popular reinforcement learning techniques to build self-learning agents

A practical guide to mastering reinforcement learning algorithms using Keras

Key Features

  • Build projects across robotics, gaming, and finance fields, putting reinforcement learning (RL) into action
  • Get to grips with Keras and practice on real-world unstructured...
Python Reinforcement Learning Projects: Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow
Python Reinforcement Learning Projects: Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow

Implement state-of-the-art deep reinforcement learning algorithms using Python and its powerful libraries

Key Features

  • Implement Q-learning and Markov models with Python and OpenAI
  • Explore the power of TensorFlow to build self-learning models
  • Eight AI...
Reinforcement Learning: With Open AI, TensorFlow and Keras Using Python
Reinforcement Learning: With Open AI, TensorFlow and Keras Using Python
Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related. You’ll then work with theories related to reinforcement learning and see the concepts that build up the reinforcement...
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...
Scala Machine Learning Projects: Build real-world machine learning and deep learning projects with Scala
Scala Machine Learning Projects: Build real-world machine learning and deep learning projects with Scala

Powerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming.

Key Features

  • Explore machine learning techniques with prominent open source Scala libraries such as Spark ML, H2O, MXNet, Zeppelin, and DeepLearning4j
  • ...
Mastering Machine Learning Algorithms: Expert techniques to implement popular machine learning algorithms and fine-tune your models
Mastering Machine Learning Algorithms: Expert techniques to implement popular machine learning algorithms and fine-tune your models

Explore and master the most important algorithms for solving complex machine learning problems.

Key Features

  • Discover high-performing machine learning algorithms and understand how they work in depth
  • One-stop solution to mastering supervised, unsupervised, and...
Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem
Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem

Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn

Key Features

  • Build a variety of Hidden Markov Models (HMM)
  • Create and apply models to any sequence of data to analyze, predict, and extract valuable...
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...
Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more
Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems.

Key Features

  • Explore deep reinforcement learning (RL), from the first principles to the latest algorithms
  • Evaluate high-profile RL methods, including value...
Linear Algebra and Probability for Computer Science Applications
Linear Algebra and Probability for Computer Science Applications

Based on the author’s course at NYU, Linear Algebra and Probability for Computer Science Applications gives an introduction to two mathematical fields that are fundamental in many areas of computer science. The course and the text are addressed to students with a very weak mathematical background. Most of the...

Probability and Statistics for Computer Scientists
Probability and Statistics for Computer Scientists

Student-Friendly Coverage of Probability, Statistical Methods, Simulation, and Modeling Tools
Incorporating feedback from instructors and researchers who used the previous edition, Probability and Statistics for Computer Scientists, Second Edition helps students understand general methods of stochastic
...

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