Mastering PyTorch. Build powerful neural network architectures using advanced PyTorch 1.x features

Ashish Ranjan Jha, Dr. Gopinath Pillai

Deep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models.The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai.By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.

Mastering Reinforcement Learning with Python. Build next-generation, self-learning models using reinforcement learning techniques and best practices

Enes Bilgin

Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL.Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you'll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning.As you advance, you’ll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray’s RLlib package. You’ll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls.By the end of this book, you’ll have mastered how to train and deploy your own RL agents for solving RL problems.

Mastering SciPy. Implement state-of-the-art techniques to visualize solutions to challenging problems in scientific computing, with the use of the SciPy stack

Francisco Javier Blanco-Silva

Mastering spaCy. An end-to-end practical guide to implementing NLP applications using the Python ecosystem

Duygu Altinok

spaCy is an industrial-grade, efficient NLP Python library. It offers various pre-trained models and ready-to-use features. Mastering spaCy provides you with end-to-end coverage of spaCy's features and real-world applications.You'll begin by installing spaCy and downloading models, before progressing to spaCy's features and prototyping real-world NLP apps. Next, you'll get familiar with visualizing with spaCy's popular visualizer displaCy. The book also equips you with practical illustrations for pattern matching and helps you advance into the world of semantics with word vectors. Statistical information extraction methods are also explained in detail. Later, you'll cover an interactive business case study that shows you how to combine all spaCy features for creating a real-world NLP pipeline. You'll implement ML models such as sentiment analysis, intent recognition, and context resolution. The book further focuses on classification with popular frameworks such as TensorFlow's Keras API together with spaCy. You'll cover popular topics, including intent classification and sentiment analysis, and use them on popular datasets and interpret the classification results.By the end of this book, you'll be able to confidently use spaCy, including its linguistic features, word vectors, and classifiers, to create your own NLP apps.

Mastering Terraform. A practical guide to building and deploying infrastructure on AWS, Azure, and GCP

Mark Tinderholt, Armon Dadgar

Advancements made in cloud technology and automation tools have led to increased challenges in defining and managing your infrastructure or app environment as code on your chosen cloud platform, integrating security, managing microservices complexity, and so on. This book takes a learning-by-doing approach to teach you how to employ Terraform features to develop efficient cloud infrastructure using real-world scenarios and best practices.The book begins by introducing you to Terraform's architecture, both as a command-line tool and a programming language, HashiCorp Configuration Language (HCL). You’ll discover best practices, usage, and architectural patterns and implement Terraform on virtual machines (VMs), Docker/Kubernetes, serverless, and multiple cloud platforms such as AWS, Azure, and GCP. The chapters also show you how to industrialize your CI/CD pipeline by integrating Terraform with other popular technologies to build, provision, deploy, and manage cloud infrastructure. You’ll gain a clear understanding of security, monitoring, troubleshooting, and cost optimization and be able to apply the concepts you’ve learned when preparing for Terraform Associate certification exams.By the end of this book, you’ll be equipped to confidently build, automate, and deliver your infrastructure and CI/CD pipelines in cloud-based applications.

Mastering Transformers. Build state-of-the-art models from scratch with advanced natural language processing techniques

Savaş Yildirim, Meysam Asgari- Chenaghlu

Transformer-based language models have dominated natural language processing (NLP) studies and have now become a new paradigm. With this book, you'll learn how to build various transformer-based NLP applications using the Python Transformers library.The book gives you an introduction to Transformers by showing you how to write your first hello-world program. You'll then learn how a tokenizer works and how to train your own tokenizer. As you advance, you'll explore the architecture of autoencoding models, such as BERT, and autoregressive models, such as GPT. You'll see how to train and fine-tune models for a variety of natural language understanding (NLU) and natural language generation (NLG) problems, including text classification, token classification, and text representation. This book also helps you to learn efficient models for challenging problems, such as long-context NLP tasks with limited computational capacity. You'll also work with multilingual and cross-lingual problems, optimize models by monitoring their performance, and discover how to deconstruct these models for interpretability and explainability. Finally, you'll be able to deploy your transformer models in a production environment.By the end of this NLP book, you'll have learned how to use Transformers to solve advanced NLP problems using advanced models.

Mastering Transformers. The Journey from BERT to Large Language Models and Stable Diffusion - Second Edition

Savaş Yildirim, Meysam Asgari- Chenaghlu

Transformer-based language models such as BERT, T5, GPT, DALL-E, and ChatGPT have dominated NLP studies and become a new paradigm. Thanks to their accurate and fast fine-tuning capabilities, transformer-based language models have been able to outperform traditional machine learning-based approaches for many challenging natural language understanding (NLU) problems.Aside from NLP, a fast-growing area in multimodal learning and generative AI has recently been established, showing promising results. Mastering Transformers will help you understand and implement multimodal solutions, including text-to-image. Computer vision solutions that are based on transformers are also explained in the book. You’ll get started by understanding various transformer models before learning how to train different autoregressive language models such as GPT and XLNet. The book will also get you up to speed with boosting model performance, as well as tracking model training using the TensorBoard toolkit. In the later chapters, you’ll focus on using vision transformers to solve computer vision problems. Finally, you’ll discover how to harness the power of transformers to model time series data and for predicting.By the end of this transformers book, you’ll have an understanding of transformer models and how to use them to solve challenges in NLP and CV.

Matematyka dyskretna dla praktyków. Algorytmy i uczenie maszynowe w Pythonie

Ryan T. White, Archana Tikayat Ray

Mimo że osiągnięcia matematyczne stały się podwalinami algorytmiki, wielu inżynierów nie w pełni rozumie reguły matematyki dyskretnej. Nawet jeśli nie stanowi to szczególnego problemu w codziennej pracy, w końcu okazuje się, że matematyka dyskretna jest niezbędna do osiągnięcia prawdziwej biegłości w operowaniu algorytmami i w pracy na danych. Co więcej, znajomość tej dziedziny bardzo ułatwia rozwiązywanie problemów z zakresu uczenia maszynowego. W ten sposób praktyczna biegłość w matematyce zauważalnie poprawia wyniki pracy inżynierów. Ta książka jest kompleksowym wprowadzeniem do matematyki dyskretnej, przydatnym dla każdego, kto chce pogłębić i ugruntować swoje umiejętności informatyczne. W zrozumiały sposób przedstawiono tu metody matematyki dyskretnej i ich zastosowanie w algorytmach i analizie danych, włączając w to techniki uczenia maszynowego. Zaprezentowano również zasady oceny złożoności obliczeniowej algorytmów i używania wyników tej oceny do zarządzania pracą procesora. Omówiono także sposoby przechowywania struktur grafowych, ich przeszukiwania i znajdywania ścieżek między wierzchołkami. Pokazano też, jak wykorzystać przedstawione informacje podczas posługiwania się bibliotekami Pythona, takimi jak scikit-learn i NumPy. W książce między innymi: terminologia i metody matematyki dyskretnej zastosowanie metod matematyki dyskretnej w algorytmach i analizie danych algebra Boole'a i kombinatoryka w podstawowych strukturach algorytmów rozwiązywanie problemów z dziedziny teorii grafów zadania związane z uczeniem maszynowym a matematyka dyskretna Matematyka dyskretna - poznaj, zrozum, zastosuj!