-
Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners, 2nd Edition
Author Dursun Delen
Using predictive analytics techniques, decision-makers can uncover hidden patterns and correlations in their data, and leverage this insight to improve a wide range of business decisions. In Predictive Analytics: Data Mining, Machine Learning and Data Science for Practiti....
Release Date 2020/12 -
Artificial Intelligence for Business, 2nd Edition
Millions of non-technical professionals and leaders want to understand Artificial Intelligence (AI) and Machine Learning (ML) — whether to improve their businesses, be more effective citizens, consumers or policymakers, or just out of sheer curiosity. Until now, most books on the subject have either.... -
Machine Learning Design Patterns
Author Valliappa Lakshmanan , Sara Robinson , Michael Munn
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. Authors Valliappa Lakshmanan, Sara Robinson, and Michael Munn catalog the first tried-and-proven methods to help engineers tackle problems that frequently crop....
Release Date 2020/12 -
Applied Regression Modeling, 3rd Edition
Master the fundamentals of regression without learning calculus with this one-stop resourceThe newly and thoroughly revised 3rd Edition of Applied Regression Modeling delivers a concise but comprehensive treatment of the application of statistical regression analysis for those with little or no back.... -
Beginning Game AI with Unity: Programming Artificial Intelligence with C#
Game developers will use this book to gain a basic knowledge of programming artificial intelligence using Unity and C#. You will not be bored learning the theory underpinning AI. Instead, you will learn by experience and practice, and complete an engaging project in each chapter.AI is the one of the.... -
Implementing AI Systems: Transform Your Business in 6 Steps
AI is one of the fastest growing corners of the tech world. But there remains one big problem: many AI projects fail. The fact is that AI is unique among IT projects. The technology requires a different mindset, in terms of understanding probabilities, data structures and complex algorithms. There i.... -
Deep Learning on Windows: Building Deep Learning Computer Vision Systems on Microsoft Windows
Build deep learning and computer vision systems using Python, TensorFlow, Keras, OpenCV, and more, right within the familiar environment of Microsoft Windows. The book starts with an introduction to tools for deep learning and computer vision tasks followed by instructions to install, configure.... -
Deep Reinforcement Learning in Unity: With Unity ML Toolkit
Gain an in-depth overview of reinforcement learning for autonomous agents in game development with Unity.This book starts with an introduction to state-based reinforcement learning algorithms involving Markov models, Bellman equations, and writing custom C# code with the aim of contrasting value and.... -
Machine Learning and AI for Healthcare : Big Data for Improved Health Outcomes
This updated second edition offers a guided tour of machine learning algorithms and architecture design. It provides real-world applications of intelligent systems in healthcare and covers the challenges of managing big data.The book has been updated with the latest research in massive data, machine.... -
ML.NET Revealed: Simple Tools for Applying Machine Learning to Your Applications
Get introduced to ML.NET, a new open source, cross-platform machine learning framework from Microsoft that is intended to democratize machine learning and enable as many developers as possible.Dive in to learn how ML.NET is designed to encapsulate complex algorithms, making it easy to consume them i.... -
Blueprints for Text Analytics Using Python
Author Jens Albrecht , Sidharth Ramachandran , Christian Wink
Turning text into valuable information is essential for many businesses looking to gain a competitive advantage. There have been many improvements in natural language processing and users have a lot of options when choosing to work on a problem. However, it’s not always c....
Release Date 2020/12 -
Practical AI on the Google Cloud Platform
Author Micheal Lanham
AI is complicated, but cloud providers have stepped in to make it easier, offering free (or affordable) state-of-the-art models and training tools to get you started. In this book, AI novices will learn how to use Google’s AI-powered cloud services to do everything....
Release Date 2020/12 -
Grokking Deep Reinforcement Learning
Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. You'll see how algorithms function and learn to develop your own DRL agents using evaluative .... -
Mastering Reinforcement Learning with Python
Get hands-on experience in creating state-of-the-art reinforcement learning agents using TensorFlow and RLlib to solve complex real-world business and industry problems with the help of expert tips and best practicesKey FeaturesUnderstand how large-scale state-of-the-art RL algorithms and approaches.... -
Hands-On Image Generation with TensorFlow
Implement various state-of-the-art architectures, such as GANs and autoencoders, for image generation using TensorFlow 2.x from scratchKey FeaturesUnderstand the different architectures for image generation, including autoencoders and GANsBuild models that can edit an image of your face, turn photos.... -
Trends in Deep Learning Methodologies
Author Vincenzo Piuri , Sandeep Raj , Angelo Genovese , Rajshree Srivastava
Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful comput....
Release Date 2020/11 -
Author Yogendra Narayan Pandey , Ayush Rastogi , Sribharath K
Apply machine and deep learning to solve some of the challenges in the oil and gas industry. The book begins with a brief discussion of the oil and gas exploration and production life cycle in the context of data flow through the different stages of industry operations. T....
Release Date 2020/11 -
Artificial Neural Networks with TensorFlow 2: ANN Architecture Machine Learning Projects
Author Poornachandra Sarang
Develop machine learning models across various domains. This book offers a single source that provides comprehensive coverage of the capabilities of TensorFlow 2 through the use of realistic, scenario-based projects.After learning what's new in TensorFlow 2, you'l....
Release Date 2020/11 -
Author Isaiah Hull
Find solutions to problems in economics and finance using tools from machine learning. ML has taken time to move into the space of academic economics. This is because empirical work in economics is concentrated on the identification of causal relationships in parsimonious....
Release Date 2020/11 -
Practical Machine Learning in JavaScript: TensorFlow.js for Web Developers
Author Charlie Gerard
Build machine learning web applications without having to learn a new language. This book will help you develop basic knowledge of machine learning concepts and applications. You’ll learn not only theory, but also dive into code samples and example projects with Tens....
Release Date 2020/11 -
Applied Neural Networks with TensorFlow 2: API Oriented Deep Learning with Python
Author Orhan Gazi Yalçın
Implement deep learning applications using TensorFlow while learning the “why” through in-depth conceptual explanations. You’ll start by learning what deep learning offers over other machine learning models. Then familiarize yourself with several technologies ....
Release Date 2020/11 -
Author Tanay Agrawal
Dive into hyperparameter tuning of machine learning models and focus on what hyperparameters are and how they work. This book discusses different techniques of hyperparameters tuning, from the basics to advanced methods.This is a step-by-step guide to hyperparameter optim....
Release Date 2020/11 -
Author Mark Treveil , Lynn Heidmann
For years, organizations have struggled to move data science, machine learning, and AI projects from the realm of experimental to having real business impact. One reason is because pivoting operations around these technologies involves more than just technology--the orche....
Release Date 2020/11 -
Deep Learning for Vision Systems
Author Mohamed Elgendy
How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand h....
Release Date 2020/11 -
Codeless Deep Learning with KNIME
Discover how to integrate KNIME Analytics Platform with deep learning libraries to implement artificial intelligence solutionsKey FeaturesBecome well-versed with KNIME Analytics Platform to perform codeless deep learningDesign and build deep learning workflows quickly and more easily using the KNIME.... -
Modern Computer Vision with PyTorch
Get to grips with deep learning techniques for building image processing applications using PyTorch with the help of code notebooks and test questionsKey FeaturesImplement solutions to 50 real-world computer vision applications using PyTorchUnderstand the theory and working mechanisms of neural netw....