Dl Engineering And Capstone
Where to Go Next
This is a short, practical index — not new material. Its job is connecting this Deep Learning curriculum to the NLP & LLM Notes that follow, so the transition f
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Jr Codex Deep Learning Notes
Level: Advanced Prerequisites: Chapter 4: Capstone — Building an Image Classifier End-to-End Time to complete: ~15 minutes
Table of Contents
- Purpose of This Chapter
- What This Deep Learning Curriculum Covered
- Mapping to the NLP & LLM Notes
- What Carries Over Directly
- What's Genuinely New
- A Suggested Path Forward
- Closing
1. Purpose of This Chapter
This is a short, practical index — not new material. Its job is connecting this Deep Learning curriculum to the NLP & LLM Notes that follow, so the transition feels like a continuation, not a fresh start.
2. What This Deep Learning Curriculum Covered
The Nine Modules, In Review
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1. Foundations — what DL is, biological/artificial
neurons, history, tooling
2. Neural Networks from — dense layers, activations,
Scratch forward prop, backprop,
gradient descent
3. Training Deep Networks — initialization, optimizers,
Effectively normalization, regularization,
LR schedules, gradient
diagnostics
4. Convolutional Neural — convolution/pooling,
Networks architectures (LeNet to
ResNet), detection,
segmentation, augmentation
5. Sequence Models — RNNs, LSTMs/GRUs,
encoder-decoder models
6. Attention & Transformers — self/multi-head
attention, positional
encoding, the full
Transformer
7. Transfer Learning — feature
extraction,
fine-tuning,
pretrained models
8. Generative Deep Learning — autoencoders,
VAEs, GANs,
diffusion
9. Engineering & Capstone — real
pipelines,
GPU
optimization,
debugging,
deployment
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3. Mapping to the NLP & LLM Notes
| This DL Curriculum | Resurfaces In the NLP & LLM Notes |
|---|---|
| Module 6 (Attention & Transformers) | NLP Notes: Word Embeddings, BERT/Encoders, GPT/Decoders — the EXACT architecture from Module 6, applied specifically to language |
| Module 6, Chapter 4, Section 7 (encoder-only vs decoder-only) | Directly determines whether a language model is BERT-style (encoder-only) or GPT-style (decoder-only) |
| Module 7 (Transfer Learning) | NLP Notes: Fine-Tuning LLMs — the SAME feature-extraction/fine-tuning spectrum, plus LoRA/PEFT (previewed in Module 7, Ch.4) |
| Module 5 (Sequence Models) | Largely HISTORICAL context for the NLP Notes — RNNs were the PREVIOUS generation of language models, before Transformers (Module 6) replaced them |
| Module 3 (Training Deep Networks) | Every optimizer, normalization, and regularization technique reappears UNCHANGED inside every Transformer-based LLM |
| Module 9 (Engineering & Capstone) | NLP Notes: LLMs in Production — the same engineering discipline (checkpointing, debugging, deployment), adapted for LLM-specific concerns (prompt handling, cost, latency) |
4. What Carries Over Directly
Skills You Won't Be Learning Cold
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Self-attention and multi-head attention the CORE
(Module 6, Ch.1-2): mechanism
inside
EVERY
modern LLM,
unchanged
The encoder-only/decoder-only DIRECTLY
distinction (Module 6, Ch.4): determines
BERT vs
GPT-style
architecture
choices
Transfer learning strategy the SAME
(Module 7): feature-
extraction-
vs-
fine-
tuning
spectrum,
now
applied
to
language
models
Training discipline optimizers,
(Module 3), debugging regularization,
(Module 9): debugging
practices
— all
IDENTICAL
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5. What's Genuinely New
Concepts the NLP & LLM Notes Introduce
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Tokenization: how raw TEXT becomes the discrete tokens
this curriculum's Transformer (Module 6)
assumes as input — a preprocessing step
with NO direct analog covered here
Pre-training masked language modeling (BERT-style)
objectives: and next-token prediction (GPT-style)
— the SPECIFIC self-supervised tasks
used to pre-train a Transformer on
raw, unlabeled text at MASSIVE scale
Prompt engineering: a genuinely NEW skill — communicating
with an ALREADY-pretrained model
through natural language
instructions, rather than training
it directly
RAG, agents, applying a pretrained LLM to
and tool use: retrieve information and take
ACTIONS, extending well
beyond this curriculum's
classification/generation
scope
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6. A Suggested Path Forward
Recommended Next Step
─────────────────────────────────────────
Continue DIRECTLY into the NLP & LLM Notes — Module 6's
Transformer architecture is the ENTIRE foundation those notes
build on; there is no need to revisit this curriculum before
starting, and the NLP Notes will reference specific chapters
here (Module 6 especially) rather than re-deriving attention
from scratch.
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7. Closing
You've now completed the entire Deep Learning curriculum — from a single artificial neuron through the complete Transformer architecture, transfer learning, generative models, and production-grade engineering practice, capped with a full end-to-end capstone project. Every architecture and technique here was chosen specifically because it either directly underlies or gets reused inside the NLP & LLM Notes that follow.
→ Continue to the NLP & LLM Notes
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