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Probabilistic Deep Learning shows how probabilistic deep learning models gives readers the tools to identify and account for uncertainty and potential errors in their results.
Starting by applying the underlying maximum likelihood principle of curve fitting to deep learning, readers will move on to using the Python-based Tensorflow Probability framework, and set up Bayesian neural networks that can state their uncertainties. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from ManningStrojové učení zaznamenalo v posledních letech pozoruhodný pokrok a dospělo od téměř nepoužitelného rozpoznávání řeči a obrazu k téměř nadlidské přesnosti, od programů, které nedokázaly porazit jen trochu zkušenějšího hráče šachu, až k přemožitelům mistrů
Discusses Open AI and Open AI Gym with relevance to Reinforcement LearningApplication of TensorFlow and Keras to Reinforcement Learning Swarm Intelligence with Python in terms of Reinforcement LearningDiscusses Google's DeepMind and the future of Reinforcement Learning
Chapter 1- Perceptrons- Introduction to Perceptrons- Working of a Perceptron- Program to understand the working of a PerceptronChapter 2: Neural Networks- Introduction to Neural Networks- Types of Neural Networks- How each neural network works- Program to understand the working of Neural
Strojové učení zaznamenalo v posledních letech pozoruhodný pokrok od téměř nepoužitelného rozpoznávání řeči a obrazu k nadlidské přesnosti. Od programů, které nedokázaly porazit jen trochu zkušenějšího hráče go, jsme dospěli k přemožiteli mistra světa. Za pokrokem ve
Summary Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF,
Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks
Summary Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology
The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has
Chapter 1Welcome to TensorFlow.jsHeadings● What is TensorFlow.js?● TensorFlow.js API○ Tensors○ Operations ○ Variables● How to install it● Use casesChapter 2Building your First ModelHeadings● Building a logistic regression classification model● Building a linear regression model●
The Complete Beginner's Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you're an absolute beginner. If you can
Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects. If you've been curious about machine learning but didn't know where to start, this is the book you've been waiting for. Focusing on the subfield of machine
Given the demand for AI and the ubiquity of JavaScript, TensorFlow.js was inevitable. With this Google framework, seasoned AI veterans and web developers alike can help propel the future of AI-driven websites. In this guide, author Gant Laborde (Google Developer Expert in machine learning and the
Learn PySparkChapter 1: Introduction to PySparkChapter 2: Data ProcessingChapter 3: Spark Structured StreamingChapter 4: AirflowChapter 5: Machine Learning Library (MLlib)Chapter 6: Supervised Machine LearningChapter 7: Unsupervised Machine LearningChapter 8: Deep Learning Using PySpark
Deep Learning: Methods and Applications provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria in mind: (1) expertise or knowledge of the authors; (2)
Build a Keras model to scale and deploy on a Kubernetes cluster We have seen an exponential growth in the use of Artificial Intelligence (AI) over last few years. AI is becoming the new electricity and is touching every industry from retail to manufacturing to healthcare to entertainment. Within
Learn math by getting creative with code Use the Python programming language to transform learning high school-level math topics like algebra, geometry, trigonometry, and calculus In Hacking Math, teacher and programmer Peter Farrell shows you how to explore core math concepts deeper than ever
Exploring the application of Bayesian probabilistic modeling techniques to musical issues, including the perception of key and meter.In Music and Probability, David Temperley explores issues in music perception and cognition from a probabilistic perspective. The application of probabilistic ideas
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning
Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword
Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz's popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. It's an ideal way to begin, whether you're new to
Companies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll
Chapter 1: Data Visualization with LeatherChapter Goal: Introduce readers to the data visualization with a simple library leatherNo of pages: 15Sub - Topics: 1. Introduction to leather2. Installation to leather3. Various types of graphs with leatherChapter 2: Introduction to the Scientific Python
A project-based approach to learning Python programming for beginners. Intriguing projects teach you how to tackle challenging problems with code. You've mastered the basics. Now you're ready to explore some of Python's more powerful tools. Real-World Python will show you how. Through a series of
The must-have companion guide to the Raspberry Pi UserGuide Raspberry Pi chose Python as its teaching language of choice toencourage a new generation of programmers to learn how to program.This approachable book serves as an ideal resource for anyonewanting to use Raspberry Pi to learn to program