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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 Quick Reference: Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras
Deep Learning Quick Reference: Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras

Dive deeper into neural networks and get your models trained, optimized with this quick reference guide

Key Features

  • A quick reference to all important deep learning concepts and their implementations
  • Essential tips, tricks, and hacks to train a variety of deep learning...
Learning Theory: 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007
Learning Theory: 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007
This book constitutes the refereed proceedings of the 20th Annual Conference on Learning Theory, COLT 2007, held in San Diego, CA, USA in June 2007.

The 41 revised full papers presented together with 5 articles on open problems and 2 invited lectures were carefully reviewed and selected from a total of 92 submissions. The papers cover a wide...

Learn Unity ML-Agents - Fundamentals of Unity Machine Learning: Incorporate new powerful ML algorithms such as Deep Reinforcement Learning for games
Learn Unity ML-Agents - Fundamentals of Unity Machine Learning: Incorporate new powerful ML algorithms such as Deep Reinforcement Learning for games

Transform games into environments using machine learning and Deep learning with Tensorflow, Keras, and Unity

Key Features

  • Learn how to apply core machine learning concepts to your games with Unity
  • Learn the Fundamentals of Reinforcement Learning and Q-Learning and apply...
Natural and Wood Fibre Reinforcement in Polymers (Rapra Review Reports)
Natural and Wood Fibre Reinforcement in Polymers (Rapra Review Reports)
The use of composite materials dates from centuries ago, when it all started with natural fibres. In ancient Egypt some 3,000 years ago, clay was reinforced by straw to build walls. Later on the natural fibre lost much of its interest.

This report examines the different fibre types available and the current research. The...

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...
Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition
Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms

Key Features

  • Explore statistics and complex mathematics for data-intensive applications
  • Discover new developments in EM algorithm, PCA, and bayesian...
Operational Risk : Modeling Analytics
Operational Risk : Modeling Analytics

Discover how to optimize business strategies from both qualitative and quantitative points of view

Operational Risk: Modeling Analytics is organized around the principle that the analysis of operational risk consists, in part, of the collection of data and the building of mathematical models to describe risk. This book is
...

Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras
Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras

Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem

Key Features

  • Build deep learning models with transfer learning principles in Python
  • implement transfer learning to solve real-world research...
Italian Demystified: A Self Teaching Guide
Italian Demystified: A Self Teaching Guide

You can learn to speak Italian in no time--capisci?

It's time to say arrivederci to your fears of learning a new language. With this easy-to-follow guide, you'll have no problema speaking and understanding Italian. Presto! Before you know it, you'll get past...

Intelligent Mobile Projects with TensorFlow: Build 10+ Artificial Intelligence apps using TensorFlow Mobile and Lite for iOS, Android, and Raspberry Pi
Intelligent Mobile Projects with TensorFlow: Build 10+ Artificial Intelligence apps using TensorFlow Mobile and Lite for iOS, Android, and Raspberry Pi

Create Deep Learning and Reinforcement Learning apps for multiple platforms with TensorFlow

Key Features

  • Build TensorFlow-powered AI applications for mobile and embedded devices
  • Learn modern AI topics such as computer vision, NLP, and deep reinforcement learning
  • ...
Qualitative Spatial Abstraction in Reinforcement Learning (Cognitive Technologies)
Qualitative Spatial Abstraction in Reinforcement Learning (Cognitive Technologies)
Teaching and learning are difficult tasks not only when people are involved but also with regard to computer programs and machines: When the teaching/learning units are too small, we cannot express sufficient context to teach a differentiated lesson; when they are too large, the complexity of the learning task can increase...
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