This book is a comprehensive guide to understanding and utilizing PyTorch 2.0 for deep learning applications. It starts with an introduction to PyTorch, its various advantages over other deep learning frameworks, and its blend with CUDA for GPU acceleration. We delve into the heart of PyTorch – tensors, learning their different types, properties, and operations. Through step-by-step examples, the reader learns to perform basic arithmetic operations on tensors, manipulate them, and understand errors related to tensor shapes.

A substantial portion of the book is dedicated to illustrating how to build simple PyTorch models. This includes uploading and preparing datasets, defining the architecture, training, and predicting. It provides hands-on exercises with a real-world dataset. The book then dives into exploring PyTorch's nn module and gives a detailed comparison of different types of networks like Feedforward, RNN, GRU, CNN, and their combination.

Further, the book delves into understanding the training process and PyTorch's optim module. It explores the overview of optimization algorithms like Gradient Descent, SGD, Mini-batch Gradient Descent, Momentum, Adagrad, and Adam. A separate chapter focuses on advanced concepts in PyTorch 2.0, like model serialization, optimization, distributed training, and PyTorch Quantization API.

In the final chapters, the book discusses the differences between TensorFlow 2.0 and PyTorch 2.0 and the step-by-step process of migrating a TensorFlow model to PyTorch 2.0 using ONNX. It provides an overview of common issues encountered during this process and how to resolve them.

Key Learnings

• A comprehensive introduction to PyTorch and CUDA for deep learning.
• Detailed understanding and operations on PyTorch tensors.
• Step-by-step guide to building simple PyTorch models.
• Insight into PyTorch's nn module and comparison of various network types.
• Overview of the training process and exploration of PyTorch's optim module.
• Understanding advanced concepts in PyTorch like model serialization and optimization.
• Knowledge of distributed training in PyTorch.
• Practical guide to using PyTorch's Quantization API.
• Differences between TensorFlow 2.0 and PyTorch 2.0.
• Guidance on migrating TensorFlow models to PyTorch using ONNX.

Table of Content

1. Introduction to Pytorch 2.0 and CUDA 11.8
2. Getting Started with Tensors
3. Advanced Tensors Operations
4. Building Neural Networks with PyTorch 2.0
5. Training Neural Networks in PyTorch 2.0
6. PyTorch 2.0 Advanced
7. Migrating from TensorFlow to PyTorch 2.0
8. End-to-End PyTorch Regression Model

Audience

A perfect and skillful book for every machine learning engineer, data scientist, AI engineer and data researcher who are passionately looking towards drawing actionable intelligence using PyTorch 2.0. Knowing Python and the basics of deep learning is all you need to sail through this book.

Designed with both beginners and professionals in mind, the book is meticulously structured to cover a broad spectrum of concepts, applications, and hands-on practices that form the core of the TensorFlow Developer Certificate exam. Starting with foundational concepts, the book guides you through the fundamental aspects of TensorFlow, Machine Learning algorithms, and Deep Learning models.

The initial chapters focus on data preprocessing, exploratory analysis, and essential tools required for building robust models. The book then delves into Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and advanced neural network techniques such as GANs and Transformer Architecture. Emphasizing practical application, each chapter is peppered with detailed explanations, code snippets, and real-world examples, allowing you to apply the concepts in various domains such as text classification, sentiment analysis, object detection, and more.

A distinctive feature of the book is its focus on various optimization and regularization techniques that enhance model performance. As the book progresses, it navigates through the complexities of deploying TensorFlow models into production. It includes exhaustive sections on TensorFlow Serving, Kubernetes Cluster, and edge computing with TensorFlow Lite. The book provides practical insights into monitoring, updating, and handling possible errors in production, ensuring a smooth transition from development to deployment.

The final chapters are devoted to preparing you for the TensorFlow Developer Certificate exam. From strategies, tips, and coding challenges to a summary of the entire learning journey, these sections serve as a robust toolkit for exam readiness. With hints and solutions provided for challenges, you can assess your knowledge and fine-tune your problem solving skills. In essence, this book is more than a mere certification guide; it's a complete roadmap to mastering TensorFlow. It aligns perfectly with the objectives of the TensorFlow Developer Certificate exam, ensuring that you are not only well-versed in the theoretical aspects but are also skilled in practical applications.

Key Learnings

• Comprehensive guide to TensorFlow, covering fundamentals to advanced topics, aiding seamless learning.
• Alignment with TensorFlow Developer Certificate exam, providing targeted preparation and confidence.
• In-depth exploration of neural networks, enhancing understanding of model architecture and function.
• Hands-on examples throughout, ensuring practical understanding and immediate applicability of concepts.
• Detailed insights into model optimization, including regularization, boosting model performance.
• Extensive focus on deployment, from TensorFlow Serving to Kubernetes, for real-world applications.
• Exploration of innovative technologies like BiLSTM, attention mechanisms, Transformers, fostering creativity.
• Step-by-step coding challenges, enhancing problem-solving skills, mirroring real-world scenarios.
• Coverage of potential errors in deployment, offering practical solutions, ensuring robust applications.
• Continual emphasis on practical, applicable knowledge, making it suitable for all levels

Table of Contents

1. Introduction to Machine Learning and TensorFlow 2.x
2. Up and Running with Neural Networks
3. Building Basic Machine Learning Models
4. Image Recognition with CNN
5. Object Detection Algorithms
6. Text Recognition and Natural Language Processing
7. Strategies to Prevent Overfitting & Underfitting
8. Advanced Neural Networks for NLP
9. Productionizing TensorFlow Models
10. Preparing for TensorFlow Developer Certificate Exam