Practical AI Bootcamp

Practical AI Bootcamp by TinkerHub Foundation. Here you will learn how to build good AI products. This learning program cover the following.

1. Finding the right machine learning model for a problem
2. Building responsible AI - Bias and other issues
3. How to train a good machine learning model - how to tune hyperparams
4. Transfer Learning - where, when and how to use ?
5. Speed and performance
6. Wraping and hosting machine learning models
7. On device machine learning
8. Some tools and tricks

Participants criteria

• Should know OOP and python
• Should know git and github
• Should know basic machine learning (different categories of ML, what is training ? What is testing ? What is dataset..etc)
• All the resources to get you started with the program is given in the resources folder. You can learn it and finish the task for joining the program!

Join the program

This bootcamp need you to have the following skills

• Python
• Github
• Machine learning

There is a task for you in the tasks folder.

1. Finish the task in a private repo.
2. Give Gopikrishnan Sasikumar access to the private repo.
3. Fill this form

We will let you know if you are selected

Program schedule

This is a 2 week Bootcamp. There will be 1 hour sessions every Monday, Wednesday, Friday and Sunday. There will be tasks to do every other days.

Day 1 (Aug 18)

Finding the right machine learning model for a problem

1. Should I use machine learning for this problem ?
2. What kind of ML task is this ?
3. Machine learning or deep learning ?

Day 2 (Aug 19)

Building responsible AI - Bias and other issues

1. Bias
2. Accountability and explainability
3. Reproducability
4. Robustness
5. Privacy

Day 3 (Aug 23)

Dataset and performance

1. Data prep
2. Data reading
3. Data Augumentation

Day 4 (Aug 25)

Techniques in training AI models

1. How to find the right learning rate ?
2. Effect of batch size
3. Epochs and early stop

Day 5 (Aug 27)

Transfer learning where when and how to use

Day 6 (Aug 29)

Wraping and hosting machine learning models

1. Building a micro service
2. Making the model as an API
3. Hosting and serving

Day 7 (Aug 31)

On device machine learning

1. Techniques to make models small
2. TensorFlow lite
3. PyTorch quantisation

Day 8 (Sep 02)

Some tools and tricks

1. Installation
2. Finding models
3. Data
4. Privacy
5. Cloud APIs and frameworks

Projects (Sep 03 to Sep 09)

You and your fellow teammates will be doing a project based on what you learnt through out the bootcamp