Phase 428 DaysIntermediate → Advanced
Phase 4 — Deep Learning
Build and train neural networks from scratch in PyTorch — from perceptrons to transformers — with diagnostic-driven iteration instead of random trial-and-error.
- Implement custom training loops with proper checkpointing and metric tracking.
- Understand CNN, RNN, LSTM, and Transformer architectures at the code level.
- Apply transfer learning to reduce data requirements for real-world tasks.
⚡ Must Know
- MLP — perceptron, hidden layers, forward pass
- Backpropagation + Chain Rule
- Activation Functions — ReLU, Sigmoid, Softmax
- Loss Functions — CrossEntropy, MSE
- Optimizers — SGD, Adam, RMSProp
- PyTorch Tensors + Autograd
- nn.Module + Custom Training Loops
- DataLoader + Dataset
- Dropout + L2 Regularization
- Batch Normalization
- CNN — filters, pooling, strides, padding
- Transfer Learning — freeze, fine-tune
- LSTM + GRU — sequence modeling
- Attention Mechanism + Transformers
- Weights & Biases — experiment tracking
✨ Good to Know
- Learning Rate Schedulers
- ResNet, VGG, EfficientNet architectures
- Image Augmentation — torchvision.transforms
- Word2Vec + GloVe embeddings
- Seq2Seq + Encoder-Decoder models
- GPU training — .cuda(), .to(device)
📚 Resources
Deep Learning Specialization
The canonical deep learning curriculum — covers backprop to transformers.
deeplearning.ai ↗PyTorch Official Tutorials
Hands-on tutorials from tensors to production deployment.
pytorch.org/tutorials ↗Andrej Karpathy — Neural Networks: Zero to Hero
Build everything from scratch — best for deep intuition.
youtube.com/karpathy ↗🏗️ Projects
CNN Image Classifier
Train a CNN on CIFAR-10 with augmentation, checkpoints, and error analysis.
LSTM Sentiment Analysis
LSTM text classifier on IMDB reviews vs simpler NLP baselines.
Mini-GPT from Scratch
Implement a compact transformer LM to deeply understand attention.