References & Further Reading

Books

1. Deep Learning by Goodfellow, Bengio, and Courville
   • The comprehensive textbook on deep learning theory
   • Free online: deeplearningbook.org
2. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
   • Practical guide with code examples
   • Covers classical ML and deep learning
3. Dive into Deep Learning by Zhang et al.
   • Interactive book with code
   • Free online: d2l.ai

Online Courses

1. Fast.ai Practical Deep Learning
   • Free, top-down approach
   • course.fast.ai
2. DeepLearning.AI Specialization (Coursera)
   • Andrew Ng’s famous course
   • Covers fundamentals to advanced topics
3. Stanford CS231n: CNNs for Visual Recognition
   • Excellent computer vision course
   • Lectures available on YouTube
4. Stanford CS224n: NLP with Deep Learning
   • Comprehensive NLP course
   • Free lecture notes and videos

Papers (Key Architectures)

1. ImageNet Classification with Deep CNNs (AlexNet, 2012)
   • Krizhevsky, Sutskever, Hinton
2. Deep Residual Learning (ResNet, 2015)
   • He, Zhang, Ren, Sun
3. Attention Is All You Need (Transformer, 2017)
   • Vaswani et al.
4. BERT: Pre-training of Deep Bidirectional Transformers (2018)
   • Devlin et al.
5. Generative Adversarial Networks (2014)
   • Goodfellow et al.

Frameworks & Documentation

1. PyTorch
   • Official docs: pytorch.org/docs
   • Tutorials: pytorch.org/tutorials
2. TensorFlow/Keras
   • TensorFlow: tensorflow.org
   • Keras: keras.io
3. Hugging Face
   • Transformers library: huggingface.co/docs
   • Pre-trained models and datasets

Communities & Forums

1. Reddit
   • r/MachineLearning
   • r/learnmachinelearning
   • r/deeplearning
2. Stack Overflow
   • pytorch tag
   • tensorflow tag
3. PyTorch Forums
   • discuss.pytorch.org
4. TensorFlow Community
   • tensorflow.org/community

Datasets

1. Kaggle Datasets
   • kaggle.com/datasets
   • Competitions and kernels
2. UCI Machine Learning Repository
   • Classic datasets for learning
3. Papers With Code
   • paperswithcode.com
   • Papers + code + datasets

Hardware & Infrastructure

1. TensorRigs
   • GPU recommendations: tensorrigs.com
   • Build guides for ML workstations
   • Hardware comparisons and benchmarks
2. Cloud Platforms
   • Google Colab (free GPU)
   • Kaggle Notebooks (free GPU/TPU)
   • AWS SageMaker
   • Google Cloud AI Platform

Research & Staying Current

1. arXiv.org
   • Latest research papers
   • cs.LG (Machine Learning) and cs.CV (Computer Vision)
2. Papers With Code
   • Track state-of-the-art results
   • Code implementations
3. Twitter/X
   • Follow researchers and practitioners
   • Quick updates on new developments

Recommended Path Forward

For specialization:

• Computer Vision: Study EfficientNet, Vision Transformers, Object Detection (YOLO, Faster R-CNN)
• NLP: Study BERT, GPT, T5, modern LLMs
• Reinforcement Learning: Study DQN, PPO, AlphaGo
• Generative Models: Study Diffusion Models, StyleGAN, DALL-E

Keep learning: - Build projects - Read 1-2 papers per week - Participate in Kaggle competitions - Contribute to open-source - Share what you learn

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Thank you for reading Practical Introduction to Deep Learning! Keep building, keep learning, and enjoy your deep learning journey!

For questions and updates, visit tensorrigs.com.