Python for Advanced Networking, Web Development, and Deep Learning
Python for Advanced Networking, Web Development, and Deep Learning
About the Bundle
Python developers interested in deep learning, web development, and networking will find this bundle to be an ideal combination of tools in his hands.
If you're new to Python network programming, "Python Networking 101" is a must-have reference that will teach you the ropes. If you're an engineer or network administrator interested in learning how to use Python for networking, this is a great resource for you.
Using WebAssembly to improve the performance of web apps, "WebAssembly Essentials" provides an extra level of advanced web development. In order to create more effective and efficient web applications, this book is essential reading for web developers.
The integration of the "PyTorch Cookbook" provides an introduction to deep learning techniques that are implemented using PyTorch. Developers and data scientists interested in deep learning applications will find this book ideal because it provides practical solutions for building and optimizing neural networks.
Taken as a whole, these books provide Python experts with a toolbox full of knowledge that can help them succeed in a wide range of fields, from state-of-the-art deep learning to professional web development and advanced networking.
Python Networking 101
Navigating essentials of networking, socket programming, AsyncIO, network testing, simulations and Ansible
Python Networking 101 is the ultimate guide for aspiring network administrators looking to build their network management and automation skills using Python. With a comprehensive and hands-on approach, this book covers the most important aspects of networking, including network fundamentals, network automation, monitoring, security, topology, and testing.
The book begins with an overview of the Python language and its libraries used for networking tasks. Each chapter then focuses on a specific networking task, providing readers with a deep understanding of the topic and practical demonstrations using Python libraries. By the end of each chapter, readers will be well-versed in the execution and implementation of these tasks.
Throughout the book, readers will learn about the best Python libraries network administrators prefer, including Netmiko, Paramiko, SNMP, Flask, AsyncIO, and more. Practical examples and exercises will help them gain hands-on experience working with these libraries to achieve various networking objectives. The book also discusses advanced network automation techniques, providing insights into network automation frameworks, such as Ansible, and how to build custom network automation solutions using Python.
By the end of the book, readers will be equipped with the knowledge to integrate Python with network management tools, making them efficient and effective network administrators.
- Master Python language and its networking libraries for network administration tasks.
- Monitor and analyze network performance and troubleshoot issues effectively.
- Enhance network security using Python libraries and best practices.
- Get well-versed with Netmiko, Paramiko, Socket, PySNMP, AsyncIO, and SimPy.
- Develop custom network services and interact with RESTful APIs using Python.
- Improve performance with asynchronous programming using AsyncIO in network applications.
- Get hands-on with Ansible to create playbooks and perform every possible network automation.
- Perform network testing and simulation, and analyze results for optimized performance.
- Manage and automate network configuration changes and ensure compliance.
- Leverage advanced network automation techniques and frameworks for efficient administration.
Table of Content
- Introduction to Python and Networking Libraries
- TCP, UDP and Socket Programming
- Working with Application Layer
- Exploring Network Automation
- Network Monitoring and Analysis
- Network Security and Python
- Working with APIs and Network Services
- Network Programming with AsyncIO
- Network Testing and Simulation
- Network Configuration Management
- Ansible and Python
"Python Networking 101" is designed to provide readers with the skills required to excel as a network administrators. The practical approach, coupled with real-world examples, ensures readers can implement the techniques learned in their professional careers. Knowing Python and the basics of computer networks is sufficient, to begin with this book.
Make code reusable and deployed for high performance web apps
Unleash the Speed: Master WebAssembly and Take Your Web Apps to the Next Level
But we go deeper. You’ll discover strategies to optimize code, leverage multi-threading, and apply cutting-edge methods like lazy loading. We also tackle memory management and dive into profiling, benchmarking, and browser tools to push performance to the limits.
This isn’t just essentials of webassembly - it’s a roadmap to learn intricacies, best practices, and solutions to common pitfalls. The goal is to equip you with the expertise to develop insanely fast applications. The future is native-level performance on the web. Join the revolution and skill up with this guide to mastering WebAssembly. Your web apps will never be the same!
- Grasp WASMs core concepts, bridging web and native apps, boosting performance seamlessly.
- Dive into tools like Emscripten and Binaryen, facilitating efficient cross language compilations to Wasm.
- Learn techniques to convert C++, Python, and Java apps, unlocking web application potential.
- Harness strategies for fine-tuning code, guaranteeing peak application performance.
- Explore multi-threading, leveraging simultaneous operations, accelerating execution times.
- Implement lazy loading and code splitting, optimizing web app responsiveness and load times.
- Dive deep into efficient memory usage, ensuring resourceful application execution.
- Proficiently use browser developer tools, dissecting and enhancing application performance.
- Enhance user experience with effective caching, ensuring rapid web application accessibility.
This book is for every programmer aspiring to put their all applications onto web browser with the same speed, efficiency and product performance. Knowing fundamentals of any programming language and basics of web app development is sufficient to tear out the book wholly.
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
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