Author Yuxi (Hayden) Liu teaches machine learning from the fundamentals to building NLP transformers and multimodal models with best practice tips and real-world examples using PyTorch, TensorFlow, scikit-learn, and pandas

Key Features:

- Discover new and updated content on NLP transformers, PyTorch, and computer vision modeling

- Includes a dedicated chapter on best practices and additional best practice tips throughout the book to improve your ML solutions

- Implement ML models, such as neural networks and linear and logistic regression, from scratch

- Purchase of the print or Kindle book includes a free PDF copy

Book Description:

The fourth edition of Python Machine Learning by Example is a comprehensive guide for beginners and experienced ML practitioners who want to learn more advanced techniques like multimodal modeling. Written by experienced machine learning author and ex-Google ML engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for ML engineers, data scientists, and analysts.

Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You'll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine.

This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.

What You Will Learn:

- Follow machine learning best practices across data preparation and model development

- Build and improve image classifiers using Convolutional Neural Networks (CNNs) and transfer learning

- Develop and fine-tune neural networks using TensorFlow and PyTorch

- Analyze sequence data and make predictions using RNNs, transformers, and CLIP

- Build classifiers using SVMs and boost performance with PCA

- Avoid overfitting using regularization, feature selection, and more

Who this book is for:

This expanded fourth edition is ideal for data scientists, ML engineers, analysts, and students with Python programming knowledge. The real-world examples, best practices, and code prepare anyone undertaking their first serious ML project.

Table of Contents

- Getting Started with Machine Learning and Python

- Building a Movie Recommendation Engine

- Predicting Online Ad Click-Through with Tree-Based Algorithms

- Predicting Online Ad Click-Through with Logistic Regression

- Predicting Stock Prices with Regression Algorithms

- Predicting Stock Prices with Artificial Neural Networks

- Mining the 20 Newsgroups Dataset with Text Analysis Techniques

- Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

- Recognizing Faces with Support Vector Machine

- Machine Learning Best Practices

- Categorizing Images of Clothing with Convolutional Neural Networks

- Making Predictions with Sequences Using Recurrent Neural Networks

- Advancing Language Understanding and Generation with Transformer Models

- Building An Image Search Engine Using Multimodal Models

- Making Decisions in Complex Environments with Reinforcement Learning

The goal of Optimal Transport (OT) is to define geometric tools that are useful to compare probability distributions. Their use dates back to 1781. Recent years have witnessed a new revolution in the spread of OT, thanks to the emergence of approximate solvers that can scale to sizes and dimensions that are relevant to data sciences. Thanks to this newfound scalability, OT is being increasingly used to unlock various problems in imaging sciences (such as color or texture processing), computer vision and graphics (for shape manipulation) or machine learning (for regression, classification and density fitting). This monograph reviews OT with a bias toward numerical methods and their applications in data sciences, and sheds lights on the theoretical properties of OT that make it particularly useful for some of these applications. Computational Optimal Transport presents an overview of the main theoretical insights that support the practical effectiveness of OT before explaining how to turn these insights into fast computational schemes. Written for readers at all levels, the authors provide descriptions of foundational theory at two-levels. Generally accessible to all readers, more advanced readers can read the specially identified more general mathematical expositions of optimal transport tailored for discrete measures. Furthermore, several chapters deal with the interplay between continuous and discrete measures, and are thus targeting a more mathematically-inclined audience. This monograph will be a valuable reference for researchers and students wishing to get a thorough understanding of Computational Optimal Transport, a mathematical gem at the interface of probability, analysis and optimization.

Embark on an exhilarating journey into the realm of modern technological marvels with this comprehensive guide. Unveil the power of algorithms that can discern patterns in vast troves of data, opening doors to innovation and insight. Whether you're a tech enthusiast, a curious mind, or a seasoned programmer, A Course in Machine Learning invites you to demystify the enigmatic world of AI and data science.

Within these pages, you'll unravel the intricacies of machine learning, guided by a seasoned expert who brings theory to life with real-world examples. Explore the algorithms that lie at the heart of self-driving cars, virtual assistants, and predictive analytics. Through hands-on exercises, sharpen your skills in creating intelligent systems that adapt and learn from experience.

Dive into the realm of neural networks and deep learning, where layers of interconnected neurons mimic the human brain's astonishing capabilities. Grasp the art of feature engineering and data preprocessing to distill meaningful insights from noisy data. With step-by-step tutorials, you'll seamlessly transition from theory to practice, developing models that can decipher handwritten text, identify objects in images, and even predict future trends.

Unlock the potential of unsupervised learning and reinforcement learning, letting algorithms uncover hidden patterns and optimize decision-making processes. From healthcare to finance, from entertainment to agriculture, the applications of machine learning are limitless. Gain the confidence to tackle real-world challenges and harness the power of data to transform industries and shape the future.

Join the ranks of innovators who are reshaping our world through machine learning's unprecedented possibilities. Whether you're a student, a professional, or simply an inquisitive mind, A Course in Machine Learning equips you with the tools to unravel the complexities of AI and build a future that's driven by intelligence and imagination. Experience the thrill of discovery as you journey through these pages, guided by the wisdom of a true trailblazer in the field.

Maxim Lapan delivers intuitive explanations and insights into complex reinforcement learning (RL) concepts, starting from the basics of RL on simple environments and tasks to modern, state-of-the-art methods

Purchase of the print or Kindle book includes a free PDF eBook

Key Features:

- Learn with concise explanations, modern libraries, and diverse applications from games to stock trading and web navigation

- Develop deep RL models, improve their stability, and efficiently solve complex environments

- New content on RL from human feedback (RLHF), MuZero, and transformers

Book Description:

Reward yourself and take this journey into RL with the third edition of Deep Reinforcement Learning Hands-On. The book takes you through the basics of RL to more advanced concepts with the help of various applications, including game playing, discrete optimization, stock trading, and web browser navigation. By walking you through landmark research papers in the field, this deep reinforcement learning book will equip you with the practical know-how of RL and the theoretical foundation to understand and implement most modern RL papers.

The book retains its strengths by providing concise and easy-to-follow explanations. You'll work through practical and diverse examples, from grid environments and games to stock trading and RL agents in web environments, to give you a well-rounded understanding of RL, its capabilities, and use cases. You'll learn about key topics, such as deep Q-networks (DQNs), policy gradient methods, continuous control problems, and highly scalable, non-gradient methods.

If you want to learn about RL using a practical approach using OpenAI Gym and PyTorch, concise explanations, and the incremental development of topics, then Deep Reinforcement Learning Hands-On, Third Edition is your ideal companion

What You Will Learn:

- Stay on the cutting edge with new content on MuZero, RL with human feedback, and LLMs

- Evaluate RL methods, including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, and D4PG

- Implement RL algorithms using PyTorch and modern RL libraries

- Build and train deep Q-networks to solve complex tasks in Atari environments

- Speed up RL models using algorithmic and engineering approaches

- Leverage advanced techniques like proximal policy optimization (PPO) for more stable training

Who this book is for:

This book is ideal for machine learning engineers, software engineers, and data scientists looking to learn and apply deep reinforcement learning in practice. It assumes familiarity with Python, calculus, and machine learning concepts. With practical examples and high-level overviews, it's also suitable for experienced professionals looking to deepen their understanding of advanced deep RL methods and apply them across industries, such as gaming and finance

Table of Contents

- What Is Reinforcement Learning?

- OpenAI Gym

- Deep Learning with PyTorch

- The Cross-Entropy Method

- Tabular Learning and the Bellman Equation

- Deep Q-Networks

- Higher-Level RL Libraries

- DQN Extensions

- Ways to Speed up RL

- Stocks Trading Using RL

- Policy Gradients - an Alternative

- Actor-Critic Methods - A2C and A3C

- The TextWorld Environment

- Web Navigation

- Continuous Action Space

- Trust Regions - PPO, TRPO, ACKTR, and SAC

- Black-Box Optimization in RL

- Advanced Exploration

- RL with Human Feedback

- MuZero

- RL in Discrete Optimization

- Multi-agent RL

- RL in Robotics

Learn to leverage Microsoft's new AI tool, Copilot, for enhanced productivity at work

In Microsoft 365 Copilot At Work: Using AI to Get the Most from Your Business Data and Favorite Apps, a team of software and AI experts delivers a comprehensive guide to unlocking the full potential of Microsoft's groundbreaking AI tool, Copilot. Written for people new to AI, as well as experienced users, this book provides a hands-on roadmap for integrating Copilot into your daily workflow. You'll find the knowledge and strategies you need to maximize your team's productivity and drive success.

The authors offer you a unique opportunity to gain a deep understanding of AI fundamentals, including machine learning, large language models, and generative AI versus summative AI. You'll also discover:

• How Copilot utilizes AI technologies to provide real-time intelligent assistance and revolutionize the way you work with Microsoft 365 apps
• Practical Implementation Strategies for project and change management, as well as practical guidance on rolling out Copilot within your organization
• Specific use cases, including Outlook, Teams, Excel, PowerPoint, and OneNote, and how Copilot can streamline tasks and boost efficiency across various Microsoft applications

Take your Copilot proficiency to the next level with advanced AI concepts, usage monitoring, and custom development techniques. Delve into Microsoft Framework Accelerator, Copilot plugins, semantic kernels, and custom plugin development, empowering you to tailor Copilot to your organization's unique needs and workflows. Get ready to revolutionize your productivity with Microsoft 365 Copilot!

Learn traditional and cutting-edge machine learning (ML) and deep learning techniques and best practices for time series forecasting, including global forecasting models, conformal prediction, and transformer architectures

Key Features:

- Apply ML and global models to improve forecasting accuracy through practical examples

- Enhance your time series toolkit by using deep learning models, including RNNs, transformers, and N-BEATS

- Learn probabilistic forecasting with conformal prediction, Monte Carlo dropout, and quantile regressions

- Purchase of the print or Kindle book includes a free eBook in PDF format

Book Description:

Predicting the future, whether it's market trends, energy demand, or website traffic, has never been more crucial. This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models. Whether you're working with traditional statistical methods or cutting-edge deep learning architectures, this book provides structured learning and best practices for both.

Starting with the basics, this data science book introduces fundamental time series concepts, such as ARIMA and exponential smoothing, before gradually progressing to advanced topics, such as machine learning for time series, deep neural networks, and transformers. As part of your fundamentals training, you'll learn preprocessing, feature engineering, and model evaluation. As you progress, you'll also explore global forecasting models, ensemble methods, and probabilistic forecasting techniques.

This new edition goes deeper into transformer architectures and probabilistic forecasting, including new content on the latest time series models, conformal prediction, and hierarchical forecasting. Whether you seek advanced deep learning insights or specialized architecture implementations, this edition provides practical strategies and new content to elevate your forecasting skills.

What You Will Learn:

- Build machine learning models for regression-based time series forecasting

- Apply powerful feature engineering techniques to enhance prediction accuracy

- Tackle common challenges like non-stationarity and seasonality

- Combine multiple forecasts using ensembling and stacking for superior results

- Explore cutting-edge advancements in probabilistic forecasting and handle intermittent or sparse time series

- Evaluate and validate your forecasts using best practices and statistical metrics

Who this book is for:

This book is ideal for data scientists, financial analysts, quantitative analysts, machine learning engineers, and researchers who need to model time-dependent data across industries, such as finance, energy, meteorology, risk analysis, and retail. Whether you are a professional looking to apply cutting-edge models to real-world problems or a student aiming to build a strong foundation in time series analysis and forecasting, this book will provide the tools and techniques you need. Familiarity with Python and basic machine learning concepts is recommended.

Table of Contents

- Introducing Time Series

- Acquiring and Processing Time Series Data

- Analyzing and Visualizing Time Series Data

- Setting a Strong Baseline Forecast

- Time Series Forecasting as Regression

- Feature Engineering for Time Series Forecasting

- Target Transformations for Time Series Forecasting

- Forecasting Time Series with Machine Learning Models

- Ensembling and Stacking

- Global Forecasting Models

- Introduction to Deep Learning

- Building Blocks of Deep Learning for Time Series

- Common Modeling Patterns for Time Series

- Attention and Transformers for Time Series

(N.B. Please use the Read Sample option to see further chapters)

Design, build, and secure scalable machine learning (ML) systems to solve real-world business problems with Python and AWS

Purchase of the print or Kindle book includes a free PDF eBook

Key Features

• Go in-depth into the ML lifecycle, from ideation and data management to deployment and scaling
• Apply risk management techniques in the ML lifecycle and design architectural patterns for various ML platforms and solutions
• Understand the generative AI lifecycle, its core technologies, and implementation risks

Book Description

David Ping, Head of GenAI and ML Solution Architecture for global industries at AWS, provides expert insights and practical examples to help you become a proficient ML solutions architect, linking technical architecture to business-related skills.

You'll learn about ML algorithms, cloud infrastructure, system design, MLOps, and how to apply ML to solve real-world business problems. David explains the generative AI project lifecycle and examines Retrieval Augmented Generation (RAG), an effective architecture pattern for generative AI applications. You'll also learn about open-source technologies, such as Kubernetes/Kubeflow, for building a data science environment and ML pipelines before building an enterprise ML architecture using AWS. As well as ML risk management and the different stages of AI/ML adoption, the biggest new addition to the handbook is the deep exploration of generative AI.

By the end of this book, you'll have gained a comprehensive understanding of AI/ML across all key aspects, including business use cases, data science, real-world solution architecture, risk management, and governance. You'll possess the skills to design and construct ML solutions that effectively cater to common use cases and follow established ML architecture patterns, enabling you to excel as a true professional in the field.

What you will learn

• Apply ML methodologies to solve business problems across industries
• Design a practical enterprise ML platform architecture
• Gain an understanding of AI risk management frameworks and techniques
• Build an end-to-end data management architecture using AWS
• Train large-scale ML models and optimize model inference latency
• Create a business application using artificial intelligence services and custom models
• Dive into generative AI with use cases, architecture patterns, and RAG

Who this book is for

This book is for solutions architects working on ML projects, ML engineers transitioning to ML solution architect roles, and MLOps engineers. Additionally, data scientists and analysts who want to enhance their practical knowledge of ML systems engineering, as well as AI/ML product managers and risk officers who want to gain an understanding of ML solutions and AI risk management, will also find this book useful. A basic knowledge of Python, AWS, linear algebra, probability, and cloud infrastructure is required before you get started with this handbook.

Table of Contents

1. Navigating the ML Lifecycle with ML Solutions Architecture
2. Exploring ML Business Use Cases
3. Exploring ML Algorithms
4. Data Management for ML
5. Exploring Open-Source ML Libraries
6. Kubernetes Container Orchestration Infrastructure Management
7. Open-Source ML Platforms
8. Building a Data Science Environment using AWS ML Services
9. Designing an Enterprise ML Architecture with AWS ML Services
10. Advanced ML Engineering
11. Building ML Solutions with AWS AI Services
12. AI Risk Management
13. Bias, Explainability, Privacy, and Adversarial Attacks

(N.B. Please use the Read Sample option to see further chapters)

A comprehensive guide for data scientists to master effective data cleaning tools and techniques

Key Features:

• Think about your data intelligently and ask the right questions
• Master data cleaning techniques using hands-on examples belonging to diverse domains
• Work with detailed, commented, well-tested code samples in Python and R

Book Description:

In data science, data analysis, or machine learning, most of the effort needed to achieve your actual purpose lies in cleaning your data. Using Python, R, and command-line tools, you will learn the essential cleaning steps performed in every production data science or data analysis pipeline. This book not only teaches you data preparation but also what questions you should ask of your data.

The book dives into the practical application of tools and techniques needed for data ingestion, anomaly detection, value imputation, and feature engineering. It also offers long-form exercises at the end of each chapter to practice the skills acquired.

You will begin by looking at data ingestion of a range of data formats. Moving on, you will impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features that are necessary for successful data analysis and visualization goals.

By the end of this book, you will have acquired a firm understanding of the data cleaning process necessary to perform real-world data science and machine learning tasks.

What You Will Learn:

• Ingest and work with common tabular, hierarchical, and other data formats
• Apply useful rules and heuristics for assessing data quality and detecting bias
• Identify and handle unreliable data and outliers in their many forms
• Impute sensible values into missing data and use sampling to fix imbalances
• Generate synthetic features that help to draw out patterns in your data
• Prepare data competently and correctly for analytic and machine learning tasks

Who this book is for:

This book is designed to benefit software developers, data scientists, aspiring data scientists, and students who are interested in data analysis or scientific computing. Basic familiarity with statistics, general concepts in machine learning, knowledge of a programming language (Python or R), and some exposure to data science are helpful. The text will also be helpful to intermediate and advanced data scientists who want to improve their rigor in data hygiene and wish for a refresher on data preparation issues.

This monograph presents the main complexity theorems in convex optimization and their corresponding algorithms. It begins with the fundamental theory of black-box optimization and proceeds to guide the reader through recent advances in structural optimization and stochastic optimization. The presentation of black-box optimization, strongly influenced by the seminal book by Nesterov, includes the analysis of cutting plane methods, as well as (accelerated) gradient descent schemes. Special attention is also given to non-Euclidean settings (relevant algorithms include Frank-Wolfe, mirror descent, and dual averaging), and discussing their relevance in machine learning. The text provides a gentle introduction to structural optimization with FISTA (to optimize a sum of a smooth and a simple non-smooth term), saddle-point mirror prox (Nemirovski's alternative to Nesterov's smoothing), and a concise description of interior point methods. In stochastic optimization it discusses stochastic gradient descent, mini-batches, random coordinate descent, and sublinear algorithms. It also briefly touches upon convex relaxation of combinatorial problems and the use of randomness to round solutions, as well as random walks based methods.

The symbiotic relationship between artificial Intelligence, supply chain and logistics transformed the way we are doing business into newer directions. The integration of advanced technologies, data-driven approaches, and machine learning algorithms enabled a new era of business transformations with efficiency, agility and competitiveness within the global marketplace.

This book helps the reader to explore the ever evolving landscape of AI within the supply chain and logistics domain. The purpose is to untangle the multi-dimensional ways, in which artificial intelligence revolutionizes these supply chain and logistic domains. This helps the readers to understand traditional methodologies and enables them to navigate complex organizational challenges pertaining to artificial intelligence, supply chain and logistics.

Supply chain and logistics are the lifeblood of global commerce, that connects interconnecting suppliers, manufacturers, distributors, retailers, and end consumers. The dynamic nature of these interconnected networks necessitates adaptability, responsiveness, and precision driven by AI unwinds the newer ways of enhancing supply chain and logistic businesses. From predictive analytics for optimizing inventory management to autonomous vehicles for streamlining transportation, AI is catalyzing a paradigm shift in how businesses strategize, operate, and deliver value.

This book covers a wide spectrum of topics including but not limited to predictive demand forecasting, route optimization, warehouse automation, risk management, and the ethical considerations surrounding AI adoption in these domains. As the AI and supply chain landscape continually evolves and becomes challengeable, this book aims to be a guiding light for professionals, enthusiasts and researchers, who would like to further explore the transformative potential of AI within supply chain and logistics. Further, this book serves as a compass to navigate that arise due to integration of AI into various critical sectors of supply chain and logistics.

We hope this book will provide illuminating path towards harnessing the true potential of AI in shaping a more efficient, resilient, and sustainable future for supply chain and logistics.

Solve real-world data problems with R and machine learning

Key Features:

• Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.6 and beyond
• Harness the power of R to build flexible, effective, and transparent machine learning models
• Learn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, Brett Lantz

Book Description:

Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data.

Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings.

This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.

What You Will Learn:

• Discover the origins of machine learning and how exactly a computer learns by example
• Prepare your data for machine learning work with the R programming language
• Classify important outcomes using nearest neighbor and Bayesian methods
• Predict future events using decision trees, rules, and support vector machines
• Forecast numeric data and estimate financial values using regression methods
• Model complex processes with artificial neural networks - the basis of deep learning
• Avoid bias in machine learning models
• Evaluate your models and improve their performance
• Connect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlow

Who this book is for:

Data scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.

Design, simulate, and program interactive robots

Key Features

• Design, simulate, build, and program an interactive autonomous mobile robot
• Leverage the power of ROS, Gazebo, and Python to enhance your robotic skills
• A hands-on guide to creating an autonomous mobile robot with the help of ROS and Python

Book Description

Robot Operating System (ROS) is one of the most popular robotics software frameworks in research and industry. It has various features for implementing different capabilities in a robot without implementing them from scratch.

This book starts by showing you the fundamentals of ROS so you understand the basics of differential robots. Then, you'll learn about robot modeling and how to design and simulate it using ROS. Moving on, we'll design robot hardware and interfacing actuators. Then, you'll learn to configure and program depth sensors and LIDARs using ROS. Finally, you'll create a GUI for your robot using the Qt framework.

By the end of this tutorial, you'll have a clear idea of how to integrate and assemble everything into a robot and how to bundle the software package.

What you will learn

• Design a differential robot from scratch
• Model a differential robot using ROS and URDF
• Simulate a differential robot using ROS and Gazebo
• Design robot hardware electronics
• Interface robot actuators with embedded boards
• Explore the interfacing of different 3D depth cameras in ROS
• Implement autonomous navigation in ChefBot
• Create a GUI for robot control

Who this book is for:

This book is for those who are conducting research in mobile robotics and autonomous navigation. As well as the robotics research domain, this book is also for the robot hobbyist community. You're expected to have a basic understanding of Linux commands and Python.

Spin glass models were introduced by physicists in the 1970s to model the statistical properties of certain magnetic materials. Over the last half century, these models have motivated a blossoming line of mathematical work with applications to multiple fields, at first sight distant from physics. This tutorial is deliberately written in a somewhat non-standard style, from several viewpoints. Rather than developing the theory in the most general setting, the authors focus on two concrete problems that are motivated by questions in statistical estimation. Their treatment is far from exhaustive, but they do not hesitate to pursue detours that are interesting, but indirectly related to the original questions posed by the examples. The authors also present a mixture of non-rigorous and rigorous techniques. The authors clearly indicate when something is proven and explain non-rigorous techniques on examples for which rigorous alternatives are available. Written by two recognized experts and based on a course given at Stanford University, this tutorial is a unique introduction to a topic that has many avenues for furthering research in statistics, mathematics, and computer science. It provides an accessible tutorial to understand and use the theories being deployed in physics for over 50 years.

Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and apps

Purchase of the print or Kindle book includes a free PDF eBook

Key Features:

• Implement state-of-the-art graph neural network architectures in Python
• Create your own graph datasets from tabular data
• Build powerful traffic forecasting, recommender systems, and anomaly detection applications

Book Description:

Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery.

Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you'll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps.

By the end of this book, you'll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more.

What You Will Learn:

• Understand the fundamental concepts of graph neural networks
• Implement graph neural networks using Python and PyTorch Geometric
• Classify nodes, graphs, and edges using millions of samples
• Predict and generate realistic graph topologies
• Combine heterogeneous sources to improve performance
• Forecast future events using topological information
• Apply graph neural networks to solve real-world problems

Who this book is for:

This book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as students looking for a comprehensive reference on this rapidly growing field. Whether you're new to graph neural networks or looking to take your knowledge to the next level, this book has something for you. Basic knowledge of machine learning and Python programming will help you get the most out of this book.

Discover the fascinating world of artificial intelligence (AI) with Artificial Intelligence Basics: A Step-by-Step Introduction to AI for Beginners. This beginner-friendly guide demystifies AI and its applications, making complex concepts accessible to readers with no prior experience. Through clear explanations and step-by-step examples, you'll explore essential AI concepts, from machine learning and neural networks to natural language processing and robotics. Perfect for students, professionals, and anyone curious about how AI shapes our world, this book covers the foundations of AI, equipping readers with a solid understanding of how AI technologies work and their impact on society.

With Artificial Intelligence Basics, you'll also learn about the ethical considerations surrounding AI, get hands-on with simple AI projects, and understand the possibilities and limitations of this groundbreaking technology. This guide offers a balanced mix of theory and practice, giving readers the confidence to dive deeper into AI or explore a career in technology. Whether you're looking to boost your career skills or just satisfy your curiosity, this book is your first step into the future of AI!

Master the fundamentals to advanced techniques of causal inference through a practical, hands-on approach with extensive R code examples and real-world applications

Key Features:

- Explore causal analysis with hands-on R tutorials and real-world examples

- Grasp complex statistical methods by taking a detailed, easy-to-follow approach

- Equip yourself with actionable insights and strategies for making data-driven decisions

- Purchase of the print or Kindle book includes a free PDF eBook

Book Description:

Determining causality in data is difficult due to confounding factors. Written by an applied scientist specializing in causal inference with over a decade of experience, Causal Inference in R provides the tools and methods you need to accurately establish causal relationships, improving data-driven decision-making.

This book helps you get to grips with foundational concepts, offering a clear understanding of causal models and their relevance in data analysis. You'll progress through chapters that blend theory with hands-on examples, illustrating how to apply advanced statistical methods to real-world scenarios. You'll discover techniques for establishing causality, from classic approaches to contemporary methods, such as propensity score matching and instrumental variables. Each chapter is enriched with detailed case studies and R code snippets, enabling you to implement concepts immediately. Beyond technical skills, this book also emphasizes critical thinking in data analysis to empower you to make informed, data-driven decisions. The chapters enable you to harness the power of causal inference in R to uncover deeper insights from data.

By the end of this book, you'll be able to confidently establish causal relationships and make data-driven decisions with precision.

What You Will Learn:

- Get a solid understanding of the fundamental concepts and applications of causal inference

- Utilize R to construct and interpret causal models

- Apply techniques for robust causal analysis in real-world data

- Implement advanced causal inference methods, such as instrumental variables and propensity score matching

- Develop the ability to apply graphical models for causal analysis

- Identify and address common challenges and pitfalls in controlled experiments for effective causal analysis

- Become proficient in the practical application of doubly robust estimation using R

Who this book is for:

This book is for data practitioners, statisticians, and researchers keen on enhancing their skills in causal inference using R, as well as individuals who aspire to make data-driven decisions in complex scenarios. It serves as a valuable resource for both beginners and experienced professionals in data analysis, public policy, economics, and social sciences. Academics and students looking to deepen their understanding of causal models and their practical implementation will also find it highly beneficial.

Table of Contents

- Introducing Causal Inference

- Unraveling Confounding and Associations

- Initiating R with a Basic Causal Inference Example

- Constructing Causality Models with Graphs

- Navigating Causal Inference through Directed Acyclic Graphs

- Employing Propensity Score Techniques

- Employing Regression Approaches for Causal Inference

- Executing A/B Testing and Controlled Experiments

- Implementing Doubly Robust Estimation

- Analyzing Instrumental Variables

- Investigating Mediation Analysis

- Exploring Sensitivity Analysis

- Scrutinizing Heterogeneity in Causal Inference

- Harnessing Causal Forests and Machine Learning Methods

- Implementing Causal Discovery in R

: A Beginner's Guide to Understanding AI: Unleashing the Power of Artificial Intelligence

Are you fascinated by Artificial Intelligence (AI) but find the subject overwhelming? Look no further - A Beginner's Guide to Understanding AI is the perfect companion to demystify the world of AI and empower you to navigate its vast landscape with confidence.

Written by industry experts, this comprehensive guidebook is meticulously crafted to cater to both the curious novice and the tech-savvy enthusiast. Whether you are an aspiring AI professional, a business owner looking to integrate AI into your operations, or simply an individual seeking a deeper understanding of this transformative technology, this book provides a solid foundation to unlock the potential of AI.

Key Features:

Clear and Engaging Explanations: Tackling complex concepts with ease, this guidebook breaks down AI jargon into understandable terms, ensuring readers grasp the fundamentals of AI while maintaining an engaging reading experience.

Step-by-Step Approach: Through a carefully curated progression of topics, this book takes readers on a journey from the basics to advanced AI applications. Each chapter builds upon the previous one, allowing for a systematic learning experience.

Practical Examples: Discover real-world applications of AI across various industries, including healthcare, finance, marketing, and more. Understand how AI is transforming these sectors and explore the possibilities AI offers for your own endeavors.

Hands-On Exercises: Reinforce your learning with practical exercises that enable you to apply AI concepts in a hands-on manner. Develop your skills by working on coding challenges, building AI models, and engaging in thought-provoking discussions.

Ethical Considerations: Dive into the ethical implications of AI - from data privacy and bias to job displacement and societal impact. Gain an understanding of the responsible use of AI and how to address potential challenges.

Future Perspectives: Explore the cutting-edge advancements and emerging trends in the AI landscape, including machine learning, deep learning, natural language processing, and robotics. Stay ahead of the curve by understanding AI's future potential.

Accessible Learning Material: This book boasts a user-friendly format, with clear diagrams, case studies, and summaries at the end of each chapter. These aids enhance comprehension and facilitate quick referencing.

Embark on an enlightening journey with A Beginner's Guide to Understanding AI - your go-to resource to bridge the gap between AI theory and practical knowledge. By the end of this book, you will possess the necessary tools to comprehend, discuss, and leverage the power of AI, revolutionizing your understanding of technology and its impact on our daily lives.

Whether you're an AI enthusiast, industry professional, or someone keen to embrace the future, this book is an essential addition to your digital library. Order your copy of A Beginner's Guide to Understanding AI today and embark on an exciting exploration of the limitless possibilities that AI has to offer.

Over 70 recipes to help you develop smart applications on Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano using the power of machine learning

Purchase of the print or Kindle book includes a free eBook in PDF format.

Key Features

• Over 20+ new recipes, including recognizing music genres and detecting objects in a scene
• Create practical examples using TensorFlow Lite for Microcontrollers, Edge Impulse, and more
• Explore cutting-edge technologies, such as on-device training for updating models without data leaving the device

Book Description

Discover the incredible world of tiny Machine Learning (tinyML) and create smart projects using real-world data sensors with the Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano.

TinyML Cookbook, Second Edition, will show you how to build unique end-to-end ML applications using temperature, humidity, vision, audio, and accelerometer sensors in different scenarios. These projects will equip you with the knowledge and skills to bring intelligence to microcontrollers. You'll train custom models from weather prediction to real-time speech recognition using TensorFlow and Edge Impulse.Expert tips will help you squeeze ML models into tight memory budgets and accelerate performance using CMSIS-DSP.

This improved edition includes new recipes featuring an LSTM neural network to recognize music genres and the Faster-Objects-More-Objects (FOMO) algorithm for detecting objects in a scene. Furthermore, you'll work on scikit-learn model deployment on microcontrollers, implement on-device training, and deploy a model using microTVM, including on a microNPU. This beginner-friendly and comprehensive book will help you stay up to date with the latest developments in the tinyML community and give you the knowledge to build unique projects with microcontrollers!

What you will learn

• Understand the microcontroller programming fundamentals
• Work with real-world sensors, such as the microphone, camera, and accelerometer
• Implement an app that responds to human voice or recognizes music genres
• Leverage transfer learning with FOMO and Keras
• Learn best practices on how to use the CMSIS-DSP library
• Create a gesture-recognition app to build a remote control
• Design a CIFAR-10 model for memory-constrained microcontrollers
• Train a neural network on microcontrollers

Who this book is for

This book is ideal for machine learning engineers or data scientists looking to build embedded/edge ML applications and IoT developers who want to add machine learning capabilities to their devices. If you're an engineer, student, or hobbyist interested in exploring tinyML, then this book is your perfect companion.

Basic familiarity with C/C++ and Python programming is a prerequisite; however, no prior knowledge of microcontrollers is necessary to get started with this book.

Table of Contents

1. Getting Ready to Unlock ML on Microcontrollers
2. Unleashing Your Creativity with Microcontrollers
3. Building a Weather Station with TensorFlow Lite for Microcontrollers
4. Using Edge Impulse and the Arduino Nano to Control LEDs with Voice Commands
5. Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico - Part 1
6. Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico - Part 2
7. Detecting Objects with Edge Impulse Using FOMO on the Raspberry Pi Pico
8. Classifying Desk Objects with TensorFlow and the Arduino Nano
9. Building a Gesture-Based Interface for YouTube Playback with Edge Impulse and the Raspberry Pi Pico

(N.B. Please use the Look Inside option to see further chapters)

Are you Interested in Learning Some of the Best Machine Learning Algorithms that Will Help you to See Some Amazing Results and Actually be Able to sort Through your Data?
> > > keep reading > > >

This guidebook is going to take some time to look at how to handle working with a Python machine learning project. We will explore all of the different parts that need to come with this, including some of the different algorithms that we are able to explore and use to help sort some of our data. This is a crucial step before diving further into machine learning with Python as this will give you a bird's eye view of what possible algorithm to use and what is best suited for your situation and what you want to do with the findings.There are so many different algorithms and more that can be used and figuring out which one is going to help with your analysis and ensure that you are able to get the results that you want is going to be tough. With the help of this guidebook, you can finally get started on your data analysis process, and figuring out the best steps to take to handle your data with the right algorithm, in no time. There is so much that we can explore when it comes to Python machine learning, and we are going to dive right into all of it. Some of the topics that we are going to discuss in this guidebook about.
You will learn:

• The basics of machine learning and why it is so important to learn.
• The importance of data and the different types of data that show up in Python and how to use these in machine learning.
• Some of the supervised learning algorithms that work with regressions, including polynomial regression, gradient descent, linear regression, and cost function.
• How to work with regularization and avoid the issue of overfitting.
• Some of the best-supervised learning algorithms of classification, including Logistic Regressions.
• How to work with non-linear classification models, like SVMs and neural networks, for your needs.
• The different validation and optimization techniques that you can use to make sure your algorithms respond the way that you want them to.
• Moving on to some unsupervised machine learning that we can use, and the best clustering algorithms along the way.
• A look at the Principal Component Analysis and the Linear Discriminant Analysis and how they compare to one another.

A lot can come into play when you work with machine learning, and when we combine the Python language, we would be able to create some of the best data analysis that we need. Combining both machine learning and Python will open doors of opportunity for us when it comes to improving or adding that little extra in our process. When you are ready to learn more about Python machine learning, scroll the top of the page and select BUY NOW .

Boolean functions are perhaps the most basic objects of study in theoretical computer science. They also arise in other areas of mathematics, including combinatorics, statistical physics, and mathematical social choice. The field of analysis of Boolean functions seeks to understand them via their Fourier transform and other analytic methods. This text gives a thorough overview of the field, beginning with the most basic definitions and proceeding to advanced topics such as hypercontractivity and isoperimetry. Each chapter includes a highlight application such as Arrow's theorem from economics, the Goldreich-Levin algorithm from cryptography/learning theory, H stad's NP-hardness of approximation results, and sharp threshold theorems for random graph properties. The book includes roughly 450 exercises and can be used as the basis of a one-semester graduate course. It should appeal to advanced undergraduates, graduate students, and researchers in computer science theory and related mathematical fields.