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Hands-On Deep Learning Architectures with Python
Concepts, tools, and techniques to explore deep learning architectures and methodologiesKey FeaturesExplore advanced deep learning architectures using various datasets and frameworksImplement deep architectures for neural network models such as CNN, RNN, GAN, and many moreDiscover design patterns and different challenges for various deep learning architecturesBook DescriptionDeep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems.Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations.By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world.What you will learnImplement CNNs, RNNs, and other commonly used architectures with PythonExplore architectures such as VGGNet, AlexNet, and GoogLeNetBuild deep learning architectures for AI applications such as face and image recognition, fraud detection, and many moreUnderstand the architectures and applications of Boltzmann machines and autoencoders with concrete examples Master artificial intelligence and neural network concepts and apply them to your architectureUnderstand deep learning architectures for mobile and embedded systemsWho this book is forIf you’re a data scientist, machine learning developer/engineer, or deep learning practitioner, or are curious about AI and want to upgrade your knowledge of various deep learning architectures, this book will appeal to you. You are expected to have some knowledge of statistics and machine learning algorithms to get the best out of this book
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Supervised Machine Learning with Python
Teach your machine to think for itself!Key FeaturesDelve into supervised learning and grasp how a machine learns from dataImplement popular machine learning algorithms from scratch, developing a deep understanding along the wayExplore some of the most popular scientific and mathematical libraries in the Python languageBook DescriptionSupervised machine learning is used in a wide range of sectors (such as finance, online advertising, and analytics) because it allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more while the system self-adjusts and makes decisions on its own. As a result, it's crucial to know how a machine “learns” under the hood.This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You’ll embark on this journey with a quick overview and see how supervised machine learning differs from unsupervised learning. Next, we explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you’ll wrap up with a brief foray into neural networks and transfer learning.By the end of this book, you’ll be equipped with hands-on techniques and will have gained the practical know-how you need to quickly and powerfully apply algorithms to new problems.What you will learnCrack how a machine learns a concept and generalize its understanding to new dataUncover the fundamental differences between parametric and non-parametric modelsImplement and grok several well-known supervised learning algorithms from scratchWork with models in domains such as ecommerce and marketingExpand your expertise and use various algorithms such as regression, decision trees, and clusteringBuild your own models capable of making predictionsDelve into the most popular approaches in deep learning such as transfer learning and neural networksWho this book is forThis book is for aspiring machine learning developers who want to get started with supervised learning. Intermediate knowledge of Python programming—and some fundamental knowledge of supervised learning—are expected.
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Generative AI for Full-Stack Development
Gain cutting-edge skills in building a full-stack web application with AI assistance. This book will guide you in creating your own travel application using React and Node.js, with MongoDB as the database, while emphasizing the use of Gen AI platforms like Perplexity.ai and Claude for quicker development and more accurate debugging. The book's step-by-step approach will help you bridge the gap between traditional web development methods and modern AI-assisted techniques, making it both accessible and insightful. It provides valuable lessons on professional web application development practices. By focusing on a practical example, the book offers hands-on experience that mirrors real-world scenarios, equipping you with relevant and in-demand skills that can be easily transferred to other projects. The book emphasizes the principles of responsive design, teaching you how to create web applications that adapt seamlessly to different screen sizes and devices. This includes using fluid grids, media queries, and optimizing layouts for usability across various platforms. You will also learn how to design, manage, and query databases using MongoDB, ensuring you can effectively handle data storage and retrieval in your applications. Most significantly, the book will introduce you to generative AI tools and prompt engineering techniques that can accelerate coding and debugging processes. This modern approach will streamline development workflows and enhance productivity. By the end of this book, you will not only have learned how to create a complete web application from backend to frontend, along with database management, but you will also have gained invaluable associated skills such as using IDEs, version control, and deploying applications efficiently and effectively with AI. What You Will Learn How to build a full-stack web application from scratchHow to use generative AI tools to enhance coding efficiency and streamline the development processHow to create user-friendly interfaces that enhance the overall experience of your web applicationsHow to design, manage, and query databases using MongoDB Who This Book Is For Frontend developers, backend developers, and full-stack developers.
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Microlearning with GenAI
This Element explores the innovative integration of microlearning and generative artificial intelligence (GenAI) in second-language education. Designed for language teachers, teacher trainers, and future educators, it offers research-informed insights and actionable strategies for designing learner-centred, technology-enhanced instruction. The Element introduces the evolution and applications of GenAI in language learning, examines its synergy with microlearning, and presents pedagogical frameworks rooted in SLA theory, cognitive load theory, and multimodal learning. Practical guidance is provided for designing, implementing, and evaluating AI-assisted microlearning tasks, focusing on interactivity, accessibility, and ethical use. The Element also explores GenAI's role in teacher professional development and considers future trends shaping language education. While celebrating the transformative potential of GenAI, it critically engages with the challenges and limitations of emerging technologies. Through a balanced blend of theory and practice, this Element equips educators with the knowledge and tools to navigate and harness the evolving AI-enhanced language learning landscape.
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Humanising Language Education in the Generative Artificial Intelligence Age
Examining the presence and influence of GenAI in the contexts of English for Academic Purposes (EAP), and written composition within university-level English as a foreign language (EFL) programmes, this book asserts a humanist view of language education informed by Critical Realism and the social realist philosophy of education.This author looks at the GenAI-language education interaction through explicit philosophical engagement, with the aim of strengthening a critical and emancipatory approach to EAP, written composition, and language education. By highlighting technological determinism and relating it to broader neoliberalist mechanisms, it provides new insights into the social, political, economic, cultural and historical contexts within which the use of GenAI technologies by language teachers and learners can be understood.Humanising Language Education in the Generative Artificial Intelligence Age will appeal to students and scholars of linguistics, education and sociology as well as organizations devoted to understanding the evolution, application, and impact of GenAI on education and language learning.
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Machine Learning with Python
This book is meant as a textbook for undergraduate and graduate students who are willing to understand essential elements of machine learning from both a theoretical and a practical perspective. The choice of the topics in the book is made based on one criterion: whether the practical utility of a certain method justifies its theoretical elaboration for students with a typical mathematical background in engineering and other quantitative fields. As a result, not only does the book contain practically useful techniques, it also presents them in a mathematical language that is accessible to both graduate and advanced undergraduate students. The textbook covers a range of topics including nearest neighbors, linear models, decision trees, ensemble learning, model evaluation and selection, dimensionality reduction, assembling various learning stages, clustering, and deep learning along with an introduction to fundamental Python packages for data science and machine learning such as NumPy, Pandas, Matplotlib, Scikit-Learn, XGBoost, and Keras with TensorFlow backend. Given the current dominant role of the Python programming language for machine learning, the book complements the theoretical presentation of each technique by its Python implementation. In this regard, two chapters are devoted to cover necessary Python programming skills. This feature makes the book self-sufficient for students with different programming backgrounds and is in sharp contrast with other books in the field that assume readers have prior Python programming experience. As such, the systematic structure of the book, along with the many examples and exercises presented, will help the readers to better grasp the content and be equipped with the practical skills required in day-to-day machine learning applications.
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Deep Learning for Coders with fastai and PyTorch
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.Train models in computer vision, natural language processing, tabular data, and collaborative filteringLearn the latest deep learning techniques that matter most in practiceImprove accuracy, speed, and reliability by understanding how deep learning models workDiscover how to turn your models into web applicationsImplement deep learning algorithms from scratchConsider the ethical implications of your workGain insight from the foreword by PyTorch cofounder, Soumith Chintala
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