Notes and resources for Brad Flaugher's Data-Focused Programming Bootcamp
- (Preferred, but not required) Install Ubuntu Linux on your PC, if you have one Install Guide, and additional notes for dual-booting with Windows NOTE if you have a Mac this will be almost impossible, but that's OK, just install docker (see below)
- (Required) Install Docker Desktop Docker.com
- (Required) Read Command Line for Beginners
- (Required) Read Chapters 1-6 of Automate The Boring Stuff with Python
- (Recommended) Learn vim via a fun game, via exercises, by reading documentation
- (Recommended) Join the bootcampers LinkedIn Group
- Note 1: Lectures are a small part of the course, most bootcamper's time will be spent working on their final potrfolio projects.
- Note 2: The 6 week course is broken into numeric and alphabetical lectures. Lectures 1-6 are technical in nature, Lectures A-E are soft-skills and history.
- Definitions: Data Scientist, Data Engineer, Data Analyst (What do we spend time doing?)
- Definitions: Machine Learning and Artificial Intelligence
- History: What kind of ML is used today? How much of this book is practically useless?
- Neural Networks: Babies and Vision
- Neural Networks: Single Cell Neural Network aka Regression in Excel
- Neural Networks: Name and Height "Regression"
- Neural Networks: When will GPT-3 "insights" become stale? Is this learning? is this engineering? is this science?
- Neural Networks: Correllating words and images
- Neural Networks: Why only study NNs for now? NNs are Decision Trees, NNs vs SVMs
- Neural Networks: Playing Pong with real neurons
- Final Project Intro: Huggable Model and Google Play Virus Model
- VIDEO: Oleh's Car Price Predictor and Source Code
- VIDEO: Fall 2022 Bootcampers Presentation WARNING LARGE FILE and Hanna's Source Code
- Help Brad with FOSS Models for Medusa
- Free captioned images from the web, LAION
- The entire web, scraped for you, Common Crawl via comcrawl
- More specialized data... Datahub and Awesome pubilc datasets
- Definitions: Unix, Linux, Command Line, DevOps, Programming Language
- History: Python and C Speed Test, SQL
- History: BERT, GPT3, DALLE, Stable Diffusion and self-driving cars.
- History: A historical perspective on technological adoption, is it fast or slow? Flavors of technological disruption. (Lateral thinking with withered technology, how many people can use spreadsheets, and Keynes quote)
- Impostor Syndrome: "10,000 Qualified data scientists" Can you trust your professor at Berkley? Who are the ML Leads at big companies? Who are the IT consultants?
- Impostor Syndrome: What does MIT Say? A review of Managing Technical Professionals.
- 9 Reasons why you'll never be a data scientist
- Huge “foundation models” are turbo-charging AI progress
- Language Models: Past, Present, and Future
- Have Computers Made Us More Productive?
- Lateral Thinking With Withered Technology
- Keynes on Next-Day Delivery
- Definitions: docker, container, ephemeral, bash
- History: SQL, what it is and why it's important (PowerBI, Tableau, Athena, BigQuery)
- Docker: Command line usage, flags, interactive mode and bash
- Docker in the cloud: How to think about the cloud, Big Providers (AWS, GCP, Azure) and Small (Linode, Oracle, etc...)
- Aside: What are Kaggle and Colab?
- Demonstration: Create a github project, spin up environment, run experiment, save python file, commit changes.
- Practice: "Head of Data" interview question, how fast can you spin up an environment?
- Docker Documentaton
- Command Line for Beginners
- Interactive SQL course @ Codecademy
- Cloud Computing Business Overview (Sep 2022)
- Docker on AWS, or the TLDR version
- Docker on GCP, or the TLDR version
- Docker on Azure
- Spotty
- Tensordock
- Definitions: Open Source, FOSS
- History: Linux, Gnu and Free Software
- Aside: Cycling team analogy, Trek, Schwinn, Homemade Bike, #2 Kid with CNC machine vs old man with saw
- Aside: “A Generation Behind” - is it true? is it useful?
- Aside: Competition and cooperation in tech, story of Google, Apple and Microsoft and Open Source.
- Choosing Technologies: How to choose a technology and not stress about it. How to handle buy vs. build and this map
- Demonstration: Numbers are Data
- Demonstration: Text is Data
- Demonstration: Images are Data
- Discussion: Data Collection, ETL and "glue code"
- Preprocessing Notebook
- NLP in 5 Minutes with Tensorflow
- Tensorflow Image Classification Example
- Few-shot classification with SetFit...
- Fundamentals of Data Engineering, Chapters 1 and 3
- 6 Step ETL with Airflow
- Airflow vs Luigi vs Kubeflow etc..
- What the Consultants say: Modules and Team Interaction
- What the consultants say: Decision Making: Blending Models and Humans
- Discussion: Testing and Documentation
- Discussion: Common data gathering tricks
- Discussion: What to do when you get stuck with a horrible dataset
- Demonstration: UNIX as IDE
- Demonstration: Infrastructrue as code
- Discussion: Which Libraries should I use?
- Discussion: What is the problem with GUIs?
- Definition: Accuracy, Precision, Recall, F1, AUC
- Discussion: Layer Types and Standard or Template Models
- Discussion: Where to start, how to adjust hyperparameters
- Discussion: How can you steal ideas?
- Definitions: AI Ethics Big 3: Explainability, Bias, and Privacy
- Discussion: Who should die? Self-Driving trolley preblems.
- Discussion: I can predict criminality, should I?
- Discussion: Are biased models useful? When?
- Google Researcher Says She Was Fired Over Paper Highlighting Bias in A.I.
- Tesla’s ‘phantom braking’ problem is getting worse, and the US government has questions
- A.I. Is Not Sentient. Why Do People Say It Is?
- The Long Road to Driverless Trucks
- Stuck on the Streets of San Francisco in a Driverless Car
- Demonstration: Tensorflow Lite, Tensorflow Serving
- Discussion: Predict is easy, train is hard (computationally)
- Demonstration: Docker + Flask
- Discussion: DevOps vs MLOps, what is special? what is the same?
- Practical MLOps Chapters 1-4
- Create an MLOps Pipeline with github and docker in minutes
- AI Template (github)
Bootcampers will spend a tremendous time working on final projects that are targeted to the bootcamper's career goals. For an example final presentation see Oleh's Video (YouTube) and Oleh's Repository (GitHub).
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent
