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Deep Learning Pipeline: Building a Deep Learning Model with TensorFlow
Author Mahmoud Hamdy , Hisham El-Amir
Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. This book shows you how to build a deep learning pipeline for real-life TensorFlow projects. You'll learn what a pipeline is and how it works so you can build ....
Release Date 2019/12 -
Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python
Author Avinash Manure , Pramod Singh
Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learnin....
Release Date 2019/12 -
Author Daniel Situnayake , Pete Warden
Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and ....
Release Date 2019/12 -
Author Qiang Yang , Yang Liu , Yong Cheng , Yan
How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private?Traditional machine learning approaches need to combine all data at one location, typically a data center, which m....
Release Date 2019/12 -
Hands-On Generative Adversarial Networks with PyTorch 1.x
Author Greg Walters , John Hany
Apply deep learning techniques and neural network methodologies to build, train, and optimize generative network modelsKey FeaturesImplement GAN architectures to generate images, text, audio, 3D models, and more Understand how GANs work and become an active contrib....
Release Date 2019/12 -
Author Bharatendra Rai
Discover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R librariesKey FeaturesImplement deep learning algorithms to build AI models with the help of tips and tricks Understand how deep learning mod....
Release Date 2019/12 -
Hands-On Reinforcement Learning with R
Author Giuseppe Ciaburro
Implement key reinforcement learning algorithms and techniques using different R packages such as the Markov chain, MDP toolbox, contextual, and OpenAI GymKey FeaturesExplore the design principles of reinforcement learning and deep reinforcement learning models Use....
Release Date 2019/12 -
Python Machine Learning - Third Edition
Author Vahid Mirjalili , Sebastian Raschka
Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.Key FeaturesThird edition of the bestselling, widely acclaimed Python machine learning bookClear and intuitive explanations take you ....
Release Date 2019/12 -
Deep Learning with TensorFlow 2 and Keras - Second Edition
Author Antonio Gulli , Amita Kapoor , Sujit Pal
Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devicesKey FeaturesIntroduces and then uses TensorFlow 2 and Keras right from the start Teaches key machine and deep learning techniques Understand....
Release Date 2019/12 -
Clustering Methodology for Symbolic Data
Author Lynne Billard , Edwin Diday
Covers everything readers need to know about clustering methodology for symbolic data—including new methods and headings—while providing a focus on multi-valued list data, interval data and histogram dataThis book presents all of the latest developments in the field of cl....
Release Date 2019/11 -
Machine Learning with Spark and Python, 2nd Edition
Author Michael Bowles
Machine Learning with Spark and Python Essential Techniques for Predictive Analytics, Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves with the addition of Spark—a ML framework from the Apache found....
Release Date 2019/11 -
Deep Neuro-Fuzzy Systems with Python: With Case Studies and Applications from the Industry
Author Yunis Ahmad Lone , Himanshu Singh
Gain insight into fuzzy logic and neural networks, and how the integration between the two models makes intelligent systems in the current world. This book simplifies the implementation of fuzzy logic and neural network concepts using Python.You’ll start by walking....
Release Date 2019/11 -
Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python
Author David Paper
Aspiring data science professionals can learn the Scikit-Learn library along with the fundamentals of machine learning with this book. The book combines the Anaconda Python distribution with the popular Scikit-Learn library to demonstrate a wide range of supervised....
Release Date 2019/11 -
Getting Started with Machine Learning in the Cloud
Author Alice LaPlante
Your company creates terabytes and even petabytes of data, but are you actually putting it to work? The majority of enterprises stumble on their way to becoming data driven. Machine learning promises to reverse that trend, and early adopters are already seeing the ....
Release Date 2019/11 -
Author Rahul Raj
Use Java and Deeplearning4j to build robust, scalable, and highly accurate AI models from scratchKey FeaturesInstall and configure Deeplearning4j to implement deep learning models from scratch Explore recipes for developing, training, and fine-tuning your neural ne....
Release Date 2019/11 -
Machine Learning for Cybersecurity Cookbook
Author Emmanuel Tsukerman
Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detectionKey FeaturesManage data of varying complexity to protect your system using the Python ecosystem Apply ML to pentestin....
Release Date 2019/11 -
Deep Learning with PyTorch 1.x
Author Laura Mitchell , Sri. Yogesh K. , Vishnu Subramanian
Build and train neural network models with high speed and flexibility in text, vision, and advanced analytics using PyTorch 1.xKey FeaturesGain a thorough understanding of the PyTorch framework and learn to implement neural network architectures Understand GPU computing ....
Release Date 2019/11 -
Hands-On Machine Learning with TensorFlow.js
Author Kai Sasaki
Get hands-on with the browser-based JavaScript library for training and deploying machine learning models effectivelyKey FeaturesBuild, train and run machine learning models in the browser using TensorFlow.js Create smart web applications from scratch with the help....
Release Date 2019/11 -
An Introduction to Machine Learning Interpretability, 2nd Edition
Author Navdeep Gill , Patrick Hall
Innovation and competition are driving analysts and data scientists toward increasingly complex predictive modeling and machine learning algorithms. This complexity makes these models accurate, but can also make their predictions difficult to understand. When accur....
Release Date 2019/10 -
Author Manohar Swamynathan
Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps awa....
Release Date 2019/10 -
Beginning Anomaly Detection Using Python-Based Deep Learning: With Keras and PyTorch
Author Suman Kalyan Adari , Sridhar Alla
Utilize this easy-to-follow beginner's guide to understand how deep learning can be applied to the task of anomaly detection. Using Keras and PyTorch in Python, the book focuses on how various deep learning models can be applied to semi-supervised and unsupervised....
Release Date 2019/10 -
Practical Deep Learning for Cloud, Mobile, and Edge
Author Meher Kasam , Siddha Ganju , Anirudh Koul
Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build pr....
Release Date 2019/10 -
Applying Deep Learning in Business
Author Federico Castanedo
According to a recent poll conducted by O’Reilly Media, most data scientists already know what AI technologies, such as deep learning, can do. Now they want to learn how to implement neural networks and deep learning to address their unique business objective. They....
Release Date 2019/10 -
Author Seth Weidman
With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine lear....
Release Date 2019/10 -
Author Xuemeng Song , Liqiang Nie , Yinglong Wang
Nowadays, fashion has become an essential aspect of people's daily life.As each outfit usually comprises several complementary items, such as a top, bottom, shoes, and accessories, a proper outfit largely relies on the harmonious matching of these items. Nevertheless, no....
Release Date 2019/10 -
Reinforcement Learning Algorithms with Python
Author Andrea Lonza
Develop self-learning algorithms and agents using TensorFlow and other Python tools, frameworks, and librariesKey FeaturesLearn, develop, and deploy advanced reinforcement learning algorithms to solve a variety of tasks Understand and develop model-free and model-b....
Release Date 2019/10 -
PyTorch 1.x Reinforcement Learning Cookbook
Author Yuxi Liu
Implement reinforcement learning techniques and algorithms with the help of real-world examples and recipesKey FeaturesUse PyTorch 1.x to design and build self-learning artificial intelligence (AI) models Implement RL algorithms to solve control and optimization ch....
Release Date 2019/10 -
Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence
Author Grant Beyleveld , Aglaé Bassens , Jon Krohn
"The authors' clear visual style provides a comprehensive look at what's currently possible with artificial neural networks as well as a glimpse of the magic that's to come."—Tim Urban, author of Wait But WhyFully Practical, Insightful Guide to Modern Deep Learn....
Release Date 2019/09 -
Author Ekaba Bisong
Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases....
Release Date 2019/09 -
Advanced Applied Deep Learning : Convolutional Neural Networks and Object Detection
Author Umberto Michelucci
Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced top....
Release Date 2019/09