Harvard CS197: AI Research Experiences & Deep Learning Guide

A review of Harvard CS197: AI Research Experiences. 21 lectures covering deep learning research, paper analysis, and productivity for AI researchers.
Author

kareem

Published

July 18, 2023

Table of contents

Reviews

Lecture 1: Exciting Advances with AI Language Models Content : Interact with language models like GPT-3’s text completion and use Codex’s code generation abilities feedback : ⭐ (1/5)

Lecture 2: The Zen of python Content : vscode,git,conad,linting and Debugging. feedback: feedback : ⭐ (1/5)

Lecture 3: Reading AI Research papers Content :

  1. Conduct a literature search to identify papers relevant to a topic of interest
  2. Difference between Reading Wide and Reading deep and how to balance between them
  3. How to use Google Scholar and paper with code feedback : ⭐⭐⭐⭐⭐ (5/5)

Lecture 4: In-Tune with Jazz Hands Content:

  1. quick intro into huggingface
  2. Tokenization
  3. Causal language modeling (CLM) feedback : ⭐⭐⭐⭐ (4/5)

Lecture 5: Lightning McTorch Content :

  1. Fine-tuning A vision Transformer
  2. Intro to pytorch lightning (Lightning)
  3. Data Loading
  4. How to Build a Neural net Module with lightning and how lightning modules work feedback : ⭐⭐⭐⭐ (4/5)

Lecture 6 & 7: Moonwalking with Pytorch Content :

  1. Pytorch Exercises
  2. Tensors
  3. Autograd and neural networks feedback : ⭐ (1/5)

Lecture 8 & 9: Experiment Organization Spakrs Joy Content :

  1. Weight and Biases
  2. Hyperparameter Search
  3. Hydra feedback : ⭐⭐⭐⭐ (4/5)

Lecture 10 & 11 : I Dreamed a Dream Content

  1. Identifying Gaps in A Research Paper
    1. CLIP and CheXzero
  2. Generating Ideas for Building a Research Paper
  3. Iterating on your research ideas feedback : ⭐⭐⭐⭐⭐ (5/5)

Lecture 12 & 13 : Today Was a Fairytale

  1. how to deconstruct the elements of a research paper and their sequence
  2. Resulting template that you can use as a general example feedback : ⭐⭐⭐⭐ (4/5)

Lecture 14 & 15: Deep Learning on Cloud Nine didn’t complete it 🙃🙃🙃

Lecture 16 & 17:Make your dreams come tuned Content

  1. high level use of Stable Diffusion using a Dreambooth template
  2. Use AWS to accelerate the training of Stable Diffusion models with GPUs
  3. HF Accelerator feedback : ⭐⭐ (2/5)

Lecture 18 : Research Productivity Power-Ups Content

  1. How update meetings and working sessions
  2. organizing your efforts on a project
  3. what is technical dept and examples on it feedback : ⭐⭐⭐⭐ (4/5)

Lecture 19 :The AI Ninja Content

  1. How to make Steady Progress
  2. Some Research Skills
  3. Discussion Questions feedback : ⭐⭐ (2/5) I found that Colah’ blog content about research is better in the context and offers a great details

Lecture 20: Bejeweled ⭐⭐⭐⭐⭐(5/5)

  1. how to make a slides to improve your research talk
  2. Assertion Evidence Approach feedback : ⭐⭐ (2/5) This is great related talk from MIT about this topic How to speak ⭐⭐⭐⭐⭐(5/5)

Lecture 21 : Model Showdown Content

  1. Statistical Testing feedback : ⭐⭐ ⭐(3/5)

Further Reading

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