🧠Deep Learning
🧠
Neural Network Fundamentals
Understand the building blocks of deep learning from perceptrons to multi-layer networks
🌱
Beginner
BeginnerBuild intuition for neural networks
What to Learn
- •Perceptron and activation functions
- •Forward propagation step by step
- •Backpropagation intuition
- •Loss functions (MSE, Cross-entropy)
- •Training with mini-batch gradient descent
Resources
- 📚3Blue1Brown: Neural Networks
- 📚Michael Nielsen: Neural Networks and Deep Learning
- 📚PyTorch 60-minute blitz
🌿
Intermediate
IntermediateDeep dive into architectures
What to Learn
- •Activation functions comparison (ReLU, GELU, Swish)
- •Batch normalization and layer normalization
- •Dropout and regularization techniques
- •Weight initialization strategies
- •Residual connections and skip connections
Resources
- 📚Deep Learning book Part II
- 📚Stanford CS231n lecture notes
- 📚Implementing networks from scratch
🌳
Advanced
AdvancedCutting-edge neural architectures
What to Learn
- •Neural architecture search (NAS)
- •Dynamic neural networks
- •Sparsity and efficient architectures
- •Knowledge distillation
- •Multi-task and meta-learning
Resources
- 📚NAS survey papers
- 📚EfficientNet and MobileNet papers
- 📚Neural network compression research