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Neural Network in PyTorch Building and Training a Simple Feedforward Neural Network for MNIST Digit Classification Using [#956535]
Free Download Neural Network in PyTorch: Building and Training a Simple Feedforward Neural Network for MNIST Digit Classification Using PyTorch (Framework-Level AI Development in Python)
English | April 11, 2025 | ASIN: B0F4N8ZGXH | 132 pages | Epub | 863.72 KB
Unlock the power of deep learning with Neural Network in PyTorch: Building and Training a Simple Feedforward Neural Network for MNIST Digit Classification Using PyTorch, the ultimate guide for aspiring AI enthusiasts, data scientists, and Python programmers eager to master cutting-edge neural network techniques. This best-selling book takes you on an immersive, step-by-step journey into the world of artificial intelligence, blending practical hands-on projects with theoretical insights to future-proof your skills in the AI revolution.
Why This Book Stands Out
Perfect for beginners and intermediates alike, this comprehensive guide demystifies the complexities of building and training neural networks using PyTorch, one of the most dynamic and Pythonic deep learning frameworks. Starting with the iconic MNIST dataset-featuring 70,000 handwritten digit images-this book walks you through creating a feedforward neural network from scratch. With clear explanations, real-world applications, and downloadable code samples, you'll gain a competitive edge by mastering AI automation, cognitive frameworks, and intelligent systems design. Whether you're automating business processes, designing multi-agent systems with RAG (Retrieval-Augmented Generation), or building next-gen AI agents, this book equips you with the tools to innovate and scale.
What You'll Learn
Foundational Skills: Dive into PyTorch fundamentals, including tensors, autograd, and data preprocessing, with a focus on classifying MNIST digits with 96.87% accuracy.
Practical Implementation: Follow a detailed, step-by-step process to construct, train, and optimize a neural network, enhanced with visualizations and best practices for debugging and fine-tuning.
Advanced Techniques: Explore agentic AI workflows, multi-agent architectures, and LLM-powered autonomous systems, integrating real-time APIs and AWS MCP servers for scalable solutions.
Workflow Automation: Leverage n8n to build intelligent multi-agent workflows, automate tasks, and integrate apps, unlocking game-changing lessons for business growth.
Future-Proof Innovation: Discover how to design self-directed AI systems, harness cognitive frameworks, and apply agentic RAG architectures to solve complex problems in computer vision and beyond.
Who This Book Is For
Ideal for Python programmers, kids learning to code, cybersecurity enthusiasts, and professionals seeking to master functional programming, Rust, or asynchronous programming. No prior deep learning experience is required-just a passion for innovation and a willingness to explore. From building smarter workflows to mastering multi-agent systems, this book is your gateway to becoming an AI leader.
Bonus Features
Hands-On Projects: Build a tumor image classifier and extend your skills to real-world applications like lung cancer detection.
Comprehensive Resources: Includes a table of contents with 10 chapters, code illustrations for key concepts, and a striking cover image featuring an intelligent AI agent overlooking a futuristic cityscape.
Community Support: Join a global community of learners with access to forums, GitHub code, and expert insights from PyTorch contributors.
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