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Week 6 Master Guide

Week 6: Deep Learning & Neural Networks From Scratch

Week 6 Daily Breakdown (7 Days)

Day 1

Week 6 — Day 1: How Neural Networks Learn — Neurons, Activations & Coding `Autograd` Backpropagation From Scratch

1 code file(s) • 2 Q&A
Day 2

Week 6 — Day 2: `PyTorch` Fundamentals — Tensors, Broadcasting, `autograd` & GPU Memory

1 code file(s) • 2 Q&A
Day 3

Week 6 — Day 3: Building Neural Networks with `torch.nn.Module`, `AdamW` & The 5-Step Training Loop

1 code file(s) • 2 Q&A
Day 4

Week 6 — Day 4: Word Embeddings (`nn.Embedding`), Sequence Models (`RNN`, `LSTM`) & Vanishing Gradients

1 code file(s) • 3 Q&A
Day 5

Week 6, Day 5: Custom `Dataset`, `DataLoader`, Dynamic Padding & Attention Masks

1 code file(s) • 3 Q&A
Day 6

Week 6, Day 6: Overfitting, Inverted Dropout, Gradient Clipping & Mixed Precision

1 code file(s) • 3 Q&A
Day 7

Week 6, Day 7 (Mini-Project): End-to-End PyTorch SRE Incident Severity Neural Classifier

1 code file(s) • 3 Q&A

Week 6: Deep Learning & Neural Networks From Scratch (PyTorch) (week6.md)

Mentor's Note (10+ YOE Staff AI Engineer POV): Welcome to Week 6! In Week 5, you learned how a single neuron (Logistic Regression) learns via Gradient Descent. In Week 6, we stack thousands of neurons into Multi-Layer Perceptrons (MLPs), Embedding Lookup Tables (nn.Embedding), and Sequence Models (RNN / LSTM / GRU), and we master PyTorch—the engine that powers OpenAI, Meta Llama, DeepSeek, vLLM, and Hugging Face!

What will you build this week?

  1. Day 1 (micrograd_engine.py): Like Andrej Karpathy's famous micrograd, you will build a working Automatic Differentiation (Autograd) & Backpropagation Engine in 80 lines of pure Python so loss.backward() and the Chain Rule of Calculus (∂L∂w=∂L∂y⋅∂y∂w\frac{\partial L}{\partial w} = \frac{\partial L}{\partial y} \cdot \frac{\partial y}{\partial w}) have zero mystery forever!
  2. Day 2–6: Master torch.Tensor, torch.autograd, nn.Module, AdamW math, nn.Embedding, LSTM gates, and Mixed Precision (FP32 vs FP16 vs BF16 vs INT8).
  3. Day 7 (pytorch_incident_classifier.py): Build and train an end-to-end PyTorch Neural Network Text Classifier on SRE incident logs!

Week 6 Architecture & Daily Folder Map

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week06_deep_learning_and_pytorch_from_scratch/ ├── week6.md # Master Week 6 Syllabus & Architecture ├── day01_neurons_activations_and_backprop_autograd/ # Day 1: Chain Rule Calculus & Micrograd Autograd │ ├── README.md │ ├── src/micrograd_engine.py │ ├── tests/test_micrograd_engine.py │ └── interview_questions.md ├── day02_pytorch_tensors_autograd_and_gpu_memory/ # Day 2: PyTorch Tensors, Broadcasting & Autograd │ ├── README.md │ ├── src/pytorch_tensor_mechanics.py │ ├── tests/test_pytorch_tensor_mechanics.py │ └── interview_questions.md ├── day03_nn_module_layers_losses_and_adamw_optimizer/ # Day 3: nn.Module, CrossEntropyLoss & AdamW Math │ ├── README.md │ ├── src/mlp_training_loop.py │ ├── tests/test_mlp_training_loop.py │ └── interview_questions.md ├── day04_word_embeddings_rnn_lstm_and_vanishing_gradients/ # Day 4: nn.Embedding, RNNs, Vanishing Gradients & LSTM │ ├── README.md │ ├── src/sequence_models_rnn_lstm.py │ ├── tests/test_sequence_models_rnn_lstm.py │ └── interview_questions.md ├── day05_custom_dataset_dataloader_padding_and_masking/ # Day 5: Dataset, DataLoader & Dynamic Padding Masks │ ├── README.md │ ├── src/dataloader_and_padding.py │ ├── tests/test_dataloader_and_padding.py │ └── interview_questions.md ├── day06_overfitting_dropout_clipping_and_fp16_bf16/ # Day 6: Dropout, Grad Clipping & FP32/FP16/BF16/INT8 │ ├── README.md │ ├── src/precision_and_diagnostics.py │ ├── tests/test_precision_and_diagnostics.py │ └── interview_questions.md └── day07_week6_mini_project_pytorch_incident_classifier/ # Day 7: End-to-End PyTorch Neural Text Classifier ├── README.md ├── src/pytorch_incident_classifier.py ├── tests/test_pytorch_incident_classifier.py └── interview_questions.md
Week 6: Week 6: Deep Learning & Neural Networks From Scratch | 24-Week AI Engineer Academy · DataArena