Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide

Ms.G.Vanitha, Associate Professor, Department of Information Technology, Bishop Heber College, Tiruchirappalli, Tamil Nadu, India. Dr.M.Kasthuri, Associate Professor, Department of Computer Science, Bishop Heber College, Tiruchirappalli, Tamil Nadu, India.

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. What You Will Learn Acquaint yourself with important elements of Machine Learning Understand the feature selection and feature engineering process Assess performance and error trade-offs for Linear Regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector machines Implement clusters to a dataset Explore the concept of Natural Processing Language and Recommendation Systems Create a ML architecture from scratch. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem. Style and approach An easy-to-follow, step-by-step guide that will help you get to grips with real -world applications of Algorithms for Machine Learning.

Hello humans & welcome to the world of machines! Specifically, machine learning & algorithms. We are about to embark on an exciting adventure through the vast and varied landscape of algorithms that power the cutting-edge field of artificial intelligence. Machine learning is changing the world as we know it. From predicting stock market trends and diagnosing diseases to powering the virtual assistants in our smartphones and enabling self-driving cars, and picking up the slack on your online dating conversations. What makes this book unique is its structure and depth. With 100 chapters, each dedicated to a different machine learning concept, this book is designed to be your ultimate guide to the world of machine learning algorithms. Whether you are a student, a data science professional, or someone curious about machine learning, this book aims to provide a comprehensive overview that is both accessible and in-depth. The algorithms covered in this book span various categories including: Classification & Regression: Learn about algorithms like Decision Trees, Random Forests, Support Vector Machines, and Logistic Regression which are used to classify data or predict numerical values. Clustering: Discover algorithms like k-Means, Hierarchical Clustering, and DBSCAN that group data points together based on similarities. Neural Networks & Deep Learning: Dive into algorithms and architectures like Perceptrons, Convolutional Neural Networks (CNN), and Long Short-Term Memory Networks (LSTM). Optimization: Understand algorithms like Gradient Descent, Genetic Algorithms, and Particle Swarm Optimization which find the best possible solutions in different scenarios. Ensemble Methods: Explore algorithms like AdaBoost, Gradient Boosting, and Random Forests which combine the predictions of multiple models for improved accuracy. Dimensionality Reduction: Learn about algorithms like Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) which reduce the number of features in a dataset while retaining important information. Reinforcement Learning: Get to know algorithms like Q-learning, Deep Q-Network (DQN), and Monte Carlo Tree Search which are used in systems that learn from their environment. Each chapter is designed as a standalone introduction to its respective algorithm. This means you can start from any chapter that catches your interest or proceed sequentially. Along with the theory, practical examples, applications, and insights into how these algorithms work under the hood are provided. This book is not just an academic endeavor but a bridge that connects theory with practical real-world applications. It's an invitation to explore, learn, and harness the power of algorithms to solve complex problems and make informed decisions. Fasten your seat belts as we dive into the mesmerizing world of machine learning algorithms. Whether you are looking to expand your knowledge, seeking inspiration, or in pursuit of technical mastery, this book should sit on your coffee table and make you look intelligent in front of all invited (and uninvited) guests.