DL 201 · Foundation course · 13 weeks · PyTorch
Foundations of neural networks: framing a task in tensor terms, then building, training, and debugging networks in PyTorch. This is the foundation deep-learning course in the program and the bridge to the advanced electives in Large Language Models and Computer Vision.
- Prerequisite: Introduction to Machine Learning
- Leads to: Advanced LLMs · Advanced Computer Vision
- Format: 3 h lecture + 2 h practice lesson per week (+ a homework lab)
- Assessment: project- and lab-based (no written exams)
Each week has three parts: a lecture (theory), an instructor-led practice lesson (live implementation and worked examples), and a lab set as homework (the graded Build/Predict/Explain work).
Course site (GitHub Pages): https://apartsin.github.io/DLCourseHIT/
- HTML · Word (.docx) · PDF
The weekly lab (homework) is built for the way students actually work, with an AI coding assistant at hand. Each lab has three parts:
- Build (AI assistant welcome): produce working code that meets a spec or hits a target metric.
- Predict & probe (the reasoning step): predict outcomes before running, then run controlled experiments.
- Explain & defend (in plain language): explain why it works, where it breaks, and what changed.
Grading weight sits on Predict and Explain, the parts an AI assistant cannot do for the student.
Each week has a lab handout (with self-check questions), a reference page of curated free sources, an instructor lesson plan (3 h lecture + 2 h practice), and a Colab practice notebook.
| Wk | Topic | Materials |
|---|---|---|
| Part I · Foundations | ||
| 1 | Deep Learning Overview & ML-to-Network Framing | Lesson · Practice nb · Self-check · Homework lab |
| 2 | Tensors & Data Representation | Lesson · Practice nb · Self-check · Homework lab |
| 3 | MLPs & Backpropagation | Lesson · Practice nb · Self-check · Homework lab |
| Part II · Training Infrastructure | ||
| 4 | Data Pipelines | Lesson · Practice nb · Self-check · Homework lab |
| 5 | Loss Functions & Metrics | Lesson · Practice nb · Self-check · Homework lab |
| 6 | Optimization | Lesson · Practice nb · Self-check · Homework lab |
| 7 | Regularization & Generalization | Lesson · Practice nb · Self-check · Homework lab |
| Part III · Architectures & Representation Learning | ||
| 8 | Convolutional Networks I | Lesson · Practice nb · Self-check · Homework lab |
| 9 | Convolutional Networks II | Lesson · Practice nb · Self-check · Homework lab |
| 10 | Recurrent Networks (RNNs) | Lesson · Practice nb · Self-check · Homework lab |
| 11 | LSTMs, GRUs & Sequence Tasks | Lesson · Practice nb · Self-check · Homework lab |
| 12 | Representation Learning | Lesson · Practice nb · Self-check · Homework lab |
| Part IV · Integration | ||
| 13 | Integration & Transfer Learning | Lesson · Practice nb · Self-check · Homework lab |
The
.htmlpages render best via the GitHub Pages site above. The notebook links open directly in Google Colab.
index.html Course home (links every week's lab, references, lesson plan, notebook)
syllabus/ Syllabus in HTML, Word, and PDF
labs/ week01..week13 student lab handouts (with self-check questions)
references/ week01..week13 curated reference pages
lessons/ week01..week13 instructor lesson plans (lecture + practice)
notebooks/ week01..week13 Colab practice notebooks (instructor, run during the practice lessons)
prereq/ Math, Python, and ML prerequisite review pages
projects/ Example briefs: 10 mid-term + 10 final projects
hit-catalogue/ HIT submission package (EN + HE syllabus, rationale, summary, questionnaire)
assets/style.css Shared house style
tools/ Generators: build_site.py, build_notebooks.py, build_hit_package.py, content.py, refs.json, lessons.py, selfcheck.py
python tools/build_site.py # index + labs + references + lessons
python tools/build_notebooks.py # Colab notebooks
python tools/build_hit_package.py # HIT catalogue .docx packageThe syllabus (syllabus/) is maintained from syllabus/syllabus.html as the source of truth; the
Word and PDF versions are regenerated from it.
hit-catalogue/ holds the department submission package in the HIT form: English syllabus, Hebrew
syllabus, rationale, catalogue summary, and committee questionnaire.
Complete: syllabus, 13 lab handouts with self-check questions, 13 reference pages, 13 instructor lesson plans, 13 Colab notebooks, and the HIT catalogue package. Lab starter solutions will be added over the term.