# Practical-Deep-Learning-for-Coders-2.0

**Repository Path**: voldemort/Practical-Deep-Learning-for-Coders-2.0

## Basic Information

- **Project Name**: Practical-Deep-Learning-for-Coders-2.0
- **Description**: Notebooks by Zachary Mueller introducing fastai2 for his study group with the addition of my own reworked notebooks for use in fastai shanghai study group on Tabular Data.
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-10-30
- **Last Updated**: 2022-07-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

![](https://github.com/muellerzr/Practical-Deep-Learning-for-Coders-2.0/blob/master/walkwithfastai2.png?raw=true)

This course will run from January 15th until May and will be live-streamed on YouTube. Each lecture will be between an hour to an hour and 15 minutes, followed by an hour to work on projects related to the course. 

**Requirements**:
* A Google account to utilize Google Colaboratory
* A Paperspace account for Natural Language Processing

The overall schedule is broken up into blocks as such:

**BLOCKS**:
* Block 1: Computer Vision
* Block 2: Tabular Neural Networks
* Block 3: Natural Language Processing

Here is the overall schedule broken down by week:
*This schedule is subject to change*

**Block 1** (January 15th - March 4th):
* Lesson 1: PETs and Custom Datasets (a warm introduction to the DataBlock API)
* Lesson 2: Image Classification Models from Scratch, Stochastic Gradient Descent, Deployment, Exploring the Documentation and Source Code
* Lesson 3: Multi-Label Classification, Dealing with Unknown Labels, and K-Fold Validation
* Lesson 4: Image Segmentation, State-of-the-Art in Computer Vision
* Lesson 5: Style Transfer, `nbdev`, and Deployment
* Lesson 6: Keypoint Regression and Object Detection
* Lesson 7: Pose Detection and Image Generation
* Lesson 8: Audio

**Block 2** (March 4th - March 25th):
* Lesson 1: Pandas Workshop and Tabular Classification
* Lesson 2: Feature Engineering and Tabular Regression
* Lesson 3: Permutation Importance, Bayesian Optimization, Cross-Validation, and Labeled Test Sets
* Lesson 4: NODE, TabNet, DeepGBM

**BLOCK 3** (April 1st - April 22nd):
* Lesson 1: Introduction to NLP and the LSTM
* Lesson 2: Full Sentiment Classification, Tokenizers, and Ensembling
* Lesson 3: Other State-of-the-Art NLP Models
* Lesson 4: Multi-Lingual Data, DeViSe

We have a Group Study discussion [here](https://forums.fast.ai/t/a-walk-with-fastai2-study-group-and-online-lectures-megathread/59929/) on the Fast.AI forums for discussing this material and asking specific questions.

* NOTE: This course does **not** have a certification or credit. This is something I have been doing for the past few semesters to help branch fellow Undergraduates at my school into the world of fastai, and this year I am making it much more available.