Practical Introduction to Deep Learning

Build neural networks from scratch with PyTorch & TensorFlow. Master CNNs, RNNs, Transformers, and more through hands-on examples. Perfect for ML practitioners ready for deep learning.

Master deep learning fundamentals and build real neural networks with both PyTorch and TensorFlow.

What You'll Learn

Build production-ready deep learning models through hands-on examples:

✅ Neural Network Fundamentals – Understand how DNNs actually work
✅ Computer Vision – Build CNNs for image classification and object detection
✅ Natural Language Processing – Create RNNs and Transformers for text tasks
✅ Transfer Learning – Leverage pre-trained models (ResNet, BERT, GPT)
✅ Advanced Architectures – GANs, Autoencoders, Attention mechanisms
✅ Production Techniques – Training optimization, regularization, debugging
✅ Real Projects – Complete end-to-end deep learning workflows

Who This Book Is For

Perfect if you are:
✅ ML practitioner ready to learn deep learning
✅ Python developer interested in AI/neural networks
✅ Data scientist expanding into deep learning
✅ Computer vision or NLP engineer
✅ Student or researcher in AI/ML
✅ Anyone who wants to build neural networks from scratch

Prerequisites:

• Basic Python (functions, classes, NumPy)
• Understanding of ML basics (training, validation)
• Familiarity with scikit-learn is helpful but not required
• Check https://amawi.gumroad.com/l/practical-ml for the prerequisites

🎓 Learning Outcomes

After completing this book, you’ll be able to:

✅ Build deep neural networks from scratch
✅ Implement CNNs for computer vision tasks
✅ Create RNNs and Transformers for NLP
✅ Apply transfer learning with pre-trained models
✅ Optimize training and debug effectively
✅ Deploy deep learning models to production
✅ Understand GANs, Attention, and advanced architectures

📚 Chapter Overview

14 Comprehensive Chapters:

1. Introduction to Deep Learning
2. Neural Network Fundamentals
3. Building Your First DNN
4. Convolutional Neural Networks
5. Working with Image Data
6. Transfer Learning
7. Recurrent Neural Networks
8. Processing Text Data
9. Training Deep Networks
10. Regularization Techniques
11. Model Evaluation & Debugging
12. Real-World Projects
13. Hands-On Exercises
14. Advanced Topics (Transformers, GANs, Autoencoders, Attention)

📊 ~300 pages of practical, actionable content

💻 All code examples included - Copy, run, learn

Formats Included

When you purchase, you get:

• 📕 PDF - Perfect for reading on any device
• 📱 EPUB - Optimized for e-readers (Kindle, Kobo, etc.)
• 🌐 Interactive HTML - Code examples you can click through
• Visit https://dlbook.tensorrigs.com/

Preview the Book

Download the PDF sample above ⬆️

✨ What Makes This Book Different

🎯 Code-First Approach – Learn by building, not just reading
🔄 Dual Framework – Master PyTorch and TensorFlow side-by-side
📊 Real Datasets – Image and text data, not toy examples
🚀 Production-Ready – Industry-grade best practices
🧠 Intuitive Explanations – Complex ideas made simple
💡 Practical Focus – Build real projects for your portfolio

💻 Interactive HTML Access

After purchase, you'll receive a unique license key via email.

Use it to unlock the full interactive HTML version at 👉 dlbook.tensorrigs.com

Features:

• 📱 Mobile-friendly design
• 🌓 Dark mode support
• 🔍 Full-text search
• 📊 Interactive code tabs (switch between PyTorch/TensorFlow)
• 📈 Progress tracking
• 🔄 Always updated with the latest content
• 🔐 Secure, lifetime access

Bonus

🎁 Lifetime updates - Get new chapters and improvements for free
🎁 Complete code - All examples ready to run

🛠 Tech Stack

Frameworks:

• PyTorch 2.0+ (primary)
• TensorFlow 2.x / Keras (alternative)

Core Libraries:

• NumPy, pandas – data processing
• matplotlib, seaborn – visualization
• torchvision, tf.keras.applications – pre-trained models
• Hugging Face Transformers – NLP tasks

Runs in:

• Jupyter notebooks
• Google Colab (free GPU!)
• Kaggle (free GPU!)
• Local Python environment

Author

Written by Abdullah Amawi, creator of TensorRigs.com - your resource for ML/DL hardware guides and practical AI education.

Money-Back Guarantee

Not satisfied? Email me within 30 days for a full refund, no questions asked.

Buy once. Learn forever. Build real DL models today.

[Purchase includes all formats + lifetime updates]