Deep Learning and Neural Networks in Practice
Deep Learning and Neural Networks in Practice
About the Bundle
For enthusiasts and professionals in AI and deep learning, this bundle offers a deep and practical understanding of two leading deep learning frameworks and foundational knowledge in neural network development.
"PyTorch Cookbook" provides a hands-on approach to PyTorch, covering a variety of neural network types and offering practical solutions to common deep learning challenges. It's an essential resource for those looking to understand and implement diverse deep learning models using PyTorch.
Complementing this, the "TensorFlow Developer Certification Guide" prepares readers for the TensorFlow Developer Certification, covering TensorFlow's core concepts and applications in deep learning. This book is invaluable for understanding TensorFlow's intricate architecture and functionalities.
The addition of "Neural Networks with Python" rounds out this bundle, offering a comprehensive introduction to building neural networks in Python. It's ideal for those starting their journey in neural network development, providing clear explanations and practical examples.
Together, these books form a complete guide for mastering deep learning techniques and frameworks, offering a path from foundational knowledge to advanced applications.
100+ Solutions across RNNs, CNNs, python tools, distributed training and graph networks
Starting a PyTorch Developer and Deep Learning Engineer career? Check out this 'PyTorch Cookbook,' a comprehensive guide with essential recipes and solutions for PyTorch and the ecosystem. The book covers PyTorch deep learning development from beginner to expert in well-written chapters.
The book simplifies neural networks, training, optimization, and deployment strategies chapter by chapter. The first part covers PyTorch basics, data preprocessing, tokenization, and vocabulary. Next, it builds CNN, RNN, Attentional Layers, and Graph Neural Networks. The book emphasizes distributed training, scalability, and multi-GPU training for real-world scenarios. Practical embedded systems, mobile development, and model compression solutions illuminate on-device AI applications. However, the book goes beyond code and algorithms. It also offers hands-on troubleshooting and debugging for end-to-end deep learning development. 'PyTorch Cookbook' covers data collection to deployment errors and provides detailed solutions to overcome them.
This book integrates PyTorch with ONNX Runtime, PySyft, Pyro, Deep Graph Library (DGL), Fastai, and Ignite, showing you how to use them for your projects. This book covers real-time inferencing, cluster training, model serving, and cross-platform compatibility. You'll learn to code deep learning architectures, work with neural networks, and manage deep learning development stages. 'PyTorch Cookbook' is a complete manual that will help you become a confident PyTorch developer and a smart Deep Learning engineer. Its clear examples and practical advice make it a must-read for anyone looking to use PyTorch and advance in deep learning.
- Comprehensive introduction to PyTorch, equipping readers with foundational skills for deep learning.
- Practical demonstrations of various neural networks, enhancing understanding through hands-on practice.
- Exploration of Graph Neural Networks (GNN), opening doors to cutting-edge research fields.
- In-depth insight into PyTorch tools and libraries, expanding capabilities beyond core functions.
- Step-by-step guidance on distributed training, enabling scalable deep learning and AI projects.
- Real-world application insights, bridging the gap between theoretical knowledge and practical execution.
- Focus on mobile and embedded development with PyTorch, leading to on-device AI.
- Emphasis on error handling and troubleshooting, preparing readers for real-world challenges.
- Advanced topics like real-time inferencing and model compression, providing future ready skill.
Table of Content
- Introduction to PyTorch 2.0
- Deep Learning Building Blocks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Natural Language Processing
- Graph Neural Networks (GNNs)
- Working with Popular PyTorch Tools
- Distributed Training and Scalability
- Mobile and Embedded Development
TensorFlow Developer Certification Guide
Crack Google’s official exam on getting skilled with managing production-grade ML models
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.
- 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
- Introduction to Machine Learning and TensorFlow 2.x
- Up and Running with Neural Networks
- Building Basic Machine Learning Models
- Image Recognition with CNN
- Object Detection Algorithms
- Text Recognition and Natural Language Processing
- Strategies to Prevent Overfitting & Underfitting
- Advanced Neural Networks for NLP
- Productionizing TensorFlow Models
- Preparing for TensorFlow Developer Certificate Exam
Neural Networks with Python
Design CNNs, Transformers, GANs and capsule networks using tensorflow and keras
"Neural Networks with Python" serves as an introductory guide for those taking their first steps into neural network development with Python. It's tailored to assist beginners in understanding the foundational elements of neural networks and to provide them with the confidence to delve deeper into this intriguing area of machine learning.
In this book, readers will embark on a learning journey, starting from the very basics of Python programming, progressing through essential concepts, and gradually building up to more complex neural network architectures. The book simplifies the learning process by using relatable examples and datasets, making the concepts accessible to everyone. You will be introduced to various neural network architectures such as Feedforward, Convolutional, and Recurrent Neural Networks, among others. Each type is explained in a clear and concise manner, with practical examples to illustrate their applications.
The book emphasizes the real-world applications and practical aspects of neural network development, rather than just theoretical knowledge. Readers will also find guidance on how to troubleshoot and refine their neural network models. The goal is to equip you with a solid understanding of how to create efficient and effective neural networks, while also being mindful of the common challenges that may arise. By the end of your journey with this book, you will have a foundational understanding of neural networks within the Python ecosystem and be prepared to apply this knowledge to real-world scenarios.
"Neural Networks with Python" aims to be your stepping stone into the vast world of machine learning, empowering you to build upon this knowledge and explore more advanced topics in the future.
- Master Python for machine learning, from setup to complex models.
- Gain flexibility with diverse neural network architectures for various problems.
- Hands-on experience in building, training, and fine-tuning neural networks.
- Learn strategic approaches for troubleshooting and optimizing neural models.
- Grasp advanced topics like autoencoders, capsule networks, and attention mechanisms.
- Acquire skills in crucial data preprocessing and augmentation techniques.
- Understand and apply optimization techniques and hyperparameter tuning.
- Implement an end-to-end machine learning project, from data to deployment.
Table of Content
- Python, TensorFlow, and your First Neural Network
- Deep Dive into Feedforward Networks
- Convolutional Networks for Visual Tasks
- Recurrent Networks for Sequence Data
- Data Generation with GANs
- Transformers for Complex Tasks
- Autoencoders for Data Compression and Generation
- Capsule Networks
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