Yuxi (Hayden) Liu Boeken

This guide focuses on practical machine learning using Python, providing insights into the latest developments in the field. It aims to enhance your understanding of various machine learning algorithms and techniques, making it suitable for both beginners and those looking to deepen their knowledge. With hands-on examples and clear explanations, readers will gain the skills needed to apply machine learning concepts effectively in real-world scenarios.

Implement reinforcement learning techniques and algorithms with the help of real-world examples and recipes Key Features Use PyTorch 1.x to design and build self-learning artificial intelligence (AI) models Implement RL algorithms to solve control and optimization challenges faced by data scientists today Apply modern RL libraries to simulate a controlled environment for your projects Book Description Reinforcement learning (RL) is a branch of machine learning that has gained popularity in recent times. It allows you to train AI models that learn from their own actions and optimize their behavior. PyTorch has also emerged as the preferred tool for training RL models because of its efficiency and ease of use. With this book, you'll explore the important RL concepts and the implementation of algorithms in PyTorch 1.x. The recipes in the book, along with real-world examples, will help you master various RL techniques, such as dynamic programming, Monte Carlo simulations, temporal difference, and Q-learning. You'll also gain insights into industry-specific applications of these techniques. Later chapters will guide you through solving problems such as the multi-armed bandit problem and the cartpole problem using the multi-armed bandit algorithm and function approximation. You'll also learn how to use Deep Q-Networks to complete Atari games, along with how to effectively implement policy gradients. Finally, you'll discover how RL techniques are applied to Blackjack, Gridworld environments, internet advertising, and the Flappy Bird game. By the end of this book, you'll have developed the skills you need to implement popular RL algorithms and use RL techniques to solve real-world problems. What you will learn Use Q-learning and the state-action-reward-state-action (SARSA) algorithm to solve various Gridworld problems Develop a multi-armed bandit algorithm to optimize display advertising Scale up learning and control processes using Deep Q-Networks Simulate Markov Decision Processes, OpenAI Gym environments, and other common control problems Select and build RL models, evaluate their performance, and optimize and deploy them Use policy gradient methods to solve continuous RL problems Who this book is for Machine learning engineers, data scientists and AI researchers looking for quick solutions to different reinforcement learning problems will find this book useful. Although prior knowledge of machine learning concepts is required, experience with PyTo..

Explore the fundamentals of machine learning through practical, real-world examples that utilize popular Python libraries like TensorFlow and scikit-learn. This book provides a hands-on approach, enabling readers to grasp essential concepts, techniques, and algorithms effectively.

Focusing on practical applications, this book guides readers through the essentials of machine learning, covering everything from foundational concepts to advanced topics like NLP transformers and multimodal models. With a hands-on approach, it incorporates best practices and real-world examples, utilizing popular frameworks such as PyTorch, TensorFlow, scikit-learn, and pandas, making complex ideas accessible for learners and practitioners alike.

This guide offers a gradual introduction to the expansive field of data science, making it accessible for beginners. It emphasizes small, manageable steps to build foundational knowledge and skills. Readers will explore key concepts, tools, and techniques in data science, empowering them to confidently navigate this complex discipline. The engaging approach encourages curiosity and fosters a deeper understanding of how data science impacts various industries.

Focusing on machine and deep learning, this guide offers a thorough exploration of PyTorch, known for its user-friendly coding framework. It is part of a bestselling series, providing readers with practical insights and techniques to effectively implement machine learning concepts. Ideal for both beginners and experienced practitioners, the book emphasizes hands-on learning and real-world applications, making complex topics accessible and engaging.

5 real-world projects to help you master deep learning concepts About This Book Master the different deep learning paradigms and build real-world projects related to text generation, sentiment analysis, fraud detection, and more Get to grips with R's impressive range of Deep Learning libraries and frameworks such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec Practical projects that show you how to implement different neural networks with helpful tips, tricks, and best practices Who This Book Is For Machine learning professionals and data scientists looking to master deep learning by implementing practical projects in R will find this book a useful resource. A knowledge of R programming and the basic concepts of deep learning is required to get the best out of this book. What You Will Learn Instrument Deep Learning models with packages such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec Apply neural networks to perform handwritten digit recognition using MXNet Get the knack of CNN models, Neural Network API, Keras, and TensorFlow for traffic sign classification Implement credit card fraud detection with Autoencoders Master reconstructing images using variational autoencoders Wade through sentiment analysis from movie reviews Run from past to future and vice versa with bidirectional Long Short-Term Memory (LSTM) networks Understand the applications of Autoencoder Neural Networks in clustering and dimensionality reduction In Detail R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. Deep Learning, as we all know, is one of the trending topics today, and is finding practical applications in a lot of domains. This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text generation, and sentiment analysis. You'll learn how to train effective neural networks in R--including convolutional neural networks, recurrent neural networks, and LSTMs--and apply them in practical scenarios. The book also highlights how neural networks can be trained using GPU capabilities. You will use popular R libraries and packages--such as MXNetR, H2O, deepnet, and more--to implement the projects. By the end of this book, you will have a better understanding of deep learning concepts and techniques and how to use them in a practical ..