Deep Learning with PyTorch Lightning. Swiftly build high-performance Artificial Intelligence (AI) models using Python

Kunal Sawarkar

Building and implementing deep learning (DL) is becoming a key skill for those who want to be at the forefront of progress.But with so much information and complex study materials out there, getting started with DL can feel quite overwhelming.Written by an AI thought leader, Deep Learning with PyTorch Lightning helps researchers build their first DL models quickly and easily without getting stuck on the complexities. With its help, you’ll be able to maximize productivity for DL projects while ensuring full flexibility – from model formulation to implementation.Throughout this book, you’ll learn how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. You’ll build a neural network architecture, deploy an application from scratch, and see how you can expand it based on your specific needs, beyond what the framework can provide.In the later chapters, you’ll also learn how to implement capabilities to build and train various models like Convolutional Neural Nets (CNN), Natural Language Processing (NLP), Time Series, Self-Supervised Learning, Semi-Supervised Learning, Generative Adversarial Network (GAN) using PyTorch Lightning.By the end of this book, you’ll be able to build and deploy DL models with confidence.

Deep Learning with TensorFlow 2 and Keras. Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API - Second Edition

Antonio Gulli, Amita Kapoor, Sujit Pal

Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before.This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.

Deep Learning with Theano. Perform large-scale numerical and scientific computations efficiently

Christopher Bourez

This book offers a complete overview of Deep Learning with Theano, a Python-based library that makes optimizing numerical expressions and deep learning models easy on CPU or GPU.The book provides some practical code examples that help the beginner understand how easy it is to build complex neural networks, while more experimented data scientists will appreciate the reach of the book, addressing supervised and unsupervised learning, generative models, reinforcement learning in the fields of image recognition, natural language processing, or game strategy.The book also discusses image recognition tasks that range from simple digit recognition, image classification, object localization, image segmentation, to image captioning. Natural language processing examples include text generation, chatbots, machine translation, and question answering. The last example deals with generating random data that looks real and solving games such as in the Open-AI gym. At the end, this book sums up the best -performing nets for each task. While early research results were based on deep stacks of neural layers, in particular, convolutional layers, the book presents the principles that improved the efficiency of these architectures, in order to help the reader build new custom nets.

Deep Reinforcement Learning Hands-On. Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more - Second Edition

Maxim Lapan

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks.With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field.In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization.In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.

Deep Reinforcement Learning with Python. Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow - Second Edition

Sudharsan Ravichandiran

With significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit.In addition to exploring RL basics and foundational concepts such as Bellman equation, Markov decision processes, and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based, and actor-critic RL methods. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth, demystifying the underlying math and demonstrating implementations through simple code examples.The book has several new chapters dedicated to new RL techniques, including distributional RL, imitation learning, inverse RL, and meta RL. You will learn to leverage stable baselines, an improvement of OpenAI’s baseline library, to effortlessly implement popular RL algorithms. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research.By the end, you will become skilled in effectively employing RL and deep RL in your real-world projects.

Designing Production-Grade and Large-Scale IoT Solutions. A comprehensive and practical guide to implementing end-to-end IoT solutions

Mohamed Abdelaziz

With the rising demand for and recent enhancements in IoT, a developer with sound knowledge of IoT is the need of the hour. This book will help you design, build, and operate large-scale E2E IoT solutions to transform your business and products, increase revenue, and reduce operational costs.Starting with an overview of how IoT technologies can help you solve your business problems, this book will be a useful guide to helping you implement end-to-end IoT solution architecture. You'll learn to select IoT devices; real-time operating systems; IoT Edge covering Edge location, software, and hardware; and the best IoT connectivity for your IoT solution. As you progress, you'll work with IoT device management, IoT data analytics, IoT platforms, and put these components to work as part of your IoT solution. You'll also be able to build IoT backend cloud from scratch by leveraging the modern app architecture paradigms and cloud-native technologies such as containers and microservices. Finally, you'll discover best practices for different operational excellence pillars, including high availability, resiliency, reliability, security, cost optimization, and high performance, which should be applied for large-scale production-grade IoT solutions.By the end of this IoT book, you'll be confident in designing, building, and operating IoT solutions.

Designing Professional Websites with Odoo Website Builder. Create and customize state-of-the-art websites and e-commerce apps for your modern business needs

Sainu Nannat

The Odoo website builder is an operational tool in the Odoo platform that allows you to design, develop, and manage a website. This book introduces and explains all the features of the Odoo website builder that will help you to be more productive while creating websites.The book starts with an overview of the Odoo website builder, its functionalities, and the tools it offers. Using descriptive illustrations and practical examples, you’ll gain detailed insights into the block operations of the Odoo website builder and learn how to work with structure blocks, features blocks, and dynamic content blocks. As you advance, you’ll discover how to use the HTML, CSS, or JS editor in Odoo website builder applications for customization. This Odoo book will take you through the different aspects of website building and show you how e-commerce websites can be designed and developed using website builder applications. You’ll build a website, manage it, and run a discussion forum in Odoo using the website builder, and apply your knowledge to add a live chat tool that can be incorporated into your website using the Odoo website builder.By the end of this book, you’ll have gained a solid understanding of the Odoo website builder and be able to leverage its features to develop your own website.

Developing Kaggle Notebooks. Pave your way to becoming a Kaggle Notebooks Grandmaster

Gabriel Preda, D. Sculley, Anthony Goldbloom

Developing Kaggle Notebooks introduces you to data analysis, with a focus on using Kaggle Notebooks to simultaneously achieve mastery in this fi eld and rise to the top of the Kaggle Notebooks tier. The book is structured as a sevenstep data analysis journey, exploring the features available in Kaggle Notebooks alongside various data analysis techniques.For each topic, we provide one or more notebooks, developing reusable analysis components through Kaggle's Utility Scripts feature, introduced progressively, initially as part of a notebook, and later extracted for use across future notebooks to enhance code reusability on Kaggle. It aims to make the notebooks' code more structured, easy to maintain, and readable.Although the focus of this book is on data analytics, some examples will guide you in preparing a complete machine learning pipeline using Kaggle Notebooks. Starting from initial data ingestion and data quality assessment, you'll move on to preliminary data analysis, advanced data exploration, feature qualifi cation to build a model baseline, and feature engineering. You'll also delve into hyperparameter tuning to iteratively refi ne your model and prepare for submission in Kaggle competitions. Additionally, the book touches on developing notebooks that leverage the power of generative AI using Kaggle Models.

Digital Forensics and Incident Response. Incident response techniques and procedures to respond to modern cyber threats - Second Edition

Gerard Johansen

An understanding of how digital forensics integrates with the overall response to cybersecurity incidents is key to securing your organization's infrastructure from attacks. This updated second edition will help you perform cutting-edge digital forensic activities and incident response.After focusing on the fundamentals of incident response that are critical to any information security team, you’ll move on to exploring the incident response framework. From understanding its importance to creating a swift and effective response to security incidents, the book will guide you with the help of useful examples. You’ll later get up to speed with digital forensic techniques, from acquiring evidence and examining volatile memory through to hard drive examination and network-based evidence. As you progress, you’ll discover the role that threat intelligence plays in the incident response process. You’ll also learn how to prepare an incident response report that documents the findings of your analysis. Finally, in addition to various incident response activities, the book will address malware analysis, and demonstrate how you can proactively use your digital forensic skills in threat hunting.By the end of this book, you’ll have learned how to efficiently investigate and report unwanted security breaches and incidents in your organization.

Distributed Data Systems with Azure Databricks. Create, deploy, and manage enterprise data pipelines

Alan Bernardo Palacio

Microsoft Azure Databricks helps you to harness the power of distributed computing and apply it to create robust data pipelines, along with training and deploying machine learning and deep learning models. Databricks' advanced features enable developers to process, transform, and explore data. Distributed Data Systems with Azure Databricks will help you to put your knowledge of Databricks to work to create big data pipelines. The book provides a hands-on approach to implementing Azure Databricks and its associated methodologies that will make you productive in no time. Complete with detailed explanations of essential concepts, practical examples, and self-assessment questions, you’ll begin with a quick introduction to Databricks core functionalities, before performing distributed model training and inference using TensorFlow and Spark MLlib. As you advance, you’ll explore MLflow Model Serving on Azure Databricks and implement distributed training pipelines using HorovodRunner in Databricks. Finally, you’ll discover how to transform, use, and obtain insights from massive amounts of data to train predictive models and create entire fully working data pipelines. By the end of this MS Azure book, you’ll have gained a solid understanding of how to work with Databricks to create and manage an entire big data pipeline.

Distributed Machine Learning with Python. Accelerating model training and serving with distributed systems

Guanhua Wang

Reducing time cost in machine learning leads to a shorter waiting time for model training and a faster model updating cycle. Distributed machine learning enables machine learning practitioners to shorten model training and inference time by orders of magnitude. With the help of this practical guide, you'll be able to put your Python development knowledge to work to get up and running with the implementation of distributed machine learning, including multi-node machine learning systems, in no time. You'll begin by exploring how distributed systems work in the machine learning area and how distributed machine learning is applied to state-of-the-art deep learning models. As you advance, you'll see how to use distributed systems to enhance machine learning model training and serving speed. You'll also get to grips with applying data parallel and model parallel approaches before optimizing the in-parallel model training and serving pipeline in local clusters or cloud environments. By the end of this book, you'll have gained the knowledge and skills needed to build and deploy an efficient data processing pipeline for machine learning model training and inference in a distributed manner.

Distributed .NET with Microsoft Orleans. Build robust and highly scalable distributed applications without worrying about complex programming patterns

Bhupesh Guptha Muthiyalu, Suneel Kumar Kunani, Reuben Bond

Building distributed applications in this modern era can be a tedious task as customers expect high availability, high performance, and improved resilience. With the help of this book, you'll discover how you can harness the power of Microsoft Orleans to build impressive distributed applications.Distributed .NET with Microsoft Orleans will demonstrate how to leverage Orleans to build highly scalable distributed applications step by step in the least possible time and with minimum effort. You'll explore some of the key concepts of Microsoft Orleans, including the Orleans programming model, runtime, virtual actors, hosting, and deployment. As you advance, you'll become well-versed with important Orleans assets such as grains, silos, timers, and persistence. Throughout the book, you'll create a distributed application by adding key components to the application as you progress through each chapter and explore them in detail.By the end of this book, you'll have developed the confidence and skills required to build distributed applications using Microsoft Orleans and deploy them in Microsoft Azure.

Django 1.0 Template Development. A practical guide to Django template development with custom tags, filters, multiple templates, caching, and more

Scott Newman, Jacob Kaplan-Moss

Django 2 by Example. Build powerful and reliable Python web applications from scratch

Antonio Melé

If you want to learn the entire process of developing professional web applications with Django 2, then this book is for you. You will walk through the creation of four professional Django 2 projects, teaching you how to solve common problems and implement best practices.You will learn how to build a blog application, a social image bookmarking website, an online shop and an e-learning platform. The book will teach you how to enhance your applications with AJAX, create RESTful APIs and set up a production environment for your Django 2 projects. The book walks you through the creation of real-world applications, solving common problems, and implementing best practices. By the end of this book, you will have a deep understanding of Django 2 and how to build advanced web applications.

Django 2. Praktyczne tworzenie aplikacji sieciowych. Wydanie II

Antonio Mele

Django jest frameworkiem do budowy profesjonalnych aplikacji sieciowych w języku Python. Cieszy się zasłużoną opinią potężnego narzędzia, którego można się szybko nauczyć. Spodoba się każdemu, kto ceni prostotę użytkowania i pragmatyczne podejście do projektowania. Z pewnością Django jest atrakcyjnym rozwiązaniem dla programistów, którzy profesjonalnie podchodzą do tworzenia aplikacji WWW i stawiają na niezawodność. To drugie, zaktualizowane i uzupełnione wydanie cenionego podręcznika pisania aplikacji sieciowych w Django. Krok po kroku pokazano w nim pełny proces tworzenia oprogramowania, od pierwszych linii kodu po wdrożenie, z akcentem na praktyczną stronę pracy dewelopera. Nie tylko zaprezentowano technikę pracy z frameworkiem, ale też opisano zasady integrowania innych popularnych technologii z projektami Django. Książka pozwala również zapoznać się z metodami rozwiązywania typowych problemów z implementacją, a także - w sposób naturalny, niejako mimochodem - nauczyć się najlepszych praktyk programistycznych z wykorzystaniem łatwych do powielenia procedur. Najważniejsze zagadnienia omówione w książce: wprowadzenie do frameworka i tworzenie praktycznego projektu aplikacji implementacja zaawansowanych funkcji aplikacji sieciowych tworzenie modelu użytkownika i zintegrowane uwierzytelnianie praca z innymi technologiami, w tym zarządzanie płatnościami tłumaczenie aplikacji na wiele języków API typu RESTful Django - dla tych, którzy chcą mieć świetne wyniki!

Django 2 Web Development Cookbook. 100 practical recipes on building scalable Python web apps with Django 2 - Third Edition

Jake Kronika, Aidas Bendoraitis

Django is a framework designed to balance rapid web development with high performance. It handles high levels of user traffic and interaction, integrates with a variety of databases, and collects and processes data in real time. This book follows a task-based approach to guide you through developing with the Django 2.1 framework, starting with setting up and configuring Docker containers and a virtual environment for your project.You'll learn how to write reusable pieces of code for your models and manage database changes. You'll work with forms and views to enter and list data, applying practical examples using templates and JavaScript together for the optimum user experience. This cookbook helps you to adjust the built-in Django administration to fit your needs and sharpen security and performance to make your web applications as robust, scalable, and dependable as possible. You'll also explore integration with Django CMS, the popular content management suite.In the final chapters, you'll learn programming and debugging tricks and discover how collecting data from different sources and providing it to others in various formats can be a breeze. By the end of the book, you'll learn how to test and deploy projects to a remote dedicated server and scale your application to meet user demands.