**Anthill Inside 2017**

Shubham Dokania

@shubhamdokania

- Recent Graduate from Delhi Technological University
- B.Tech in Mathematics and Computing Engineering

- Machine Learning Instructor at Coding Blocks.
- Research Intern at IIIT-D
- Supervised by Dr. Ganesh Bagler
- Working on drug-side-efffect predictions

- A few papers (accepted and under-review) on Evolutionary Optimization, Vision and ML.
- Independent Study on Deep Learning, Reinforcement Learning.

- Introduction to Machine Learning
- Four Sessions (~90 mins each)
- Mathematical Conceptualisation
- Project Implementation

- Introduction and warm-up
- CNN architectures and applications
- Transfer Learning & Sequence Processing
- Reinforcement Learning

- History and Introduction to DL
- Applications (Industrial and daily)
- Convex Optimization
- Hands-on with code

- Introduction and some History
- The Gartner Hype Cycle
- Applications
- Image classification/recognnition
- Image captioning/VQA
- Sequence processing
- Speech Recognition

Some examples of transformation operations

Libraries: Numpy, Matplotlib

- Caffe
- Torch
- PyTorch
- Chainer
- Lasagne
- Tensorflow
- Theano
- Keras
- And more...

~10-15 mins

Padding, Strides etc...

- What is it?
- Applications

- Accuracy and Loss
- Training Time
- Improvements

Let's build both!

- AutoEncoder models
- Sequence to sequence learning
- Neural Machine Translation
- Sentiment classification

- And more...

- Supervised, Unsupervised and RL
- Learning through Rewards
- The algorithm powering AlphaGo

- A Dynamic Programming approach
- Update function
- Toy task with simple graph

- Feature based learning
- Approximate rewards
- Basis for Deep Q-Networks

- Game-play understanding
- The Reward function
- DQN on Atari Ms-Pacman
- Hands-on project

- Conclusions
- Some more examples
- What to do after the workshop
- Resources and papers to follow