AI Basics (Theory)
- Introduction and scope
- Understanding Intelligent agents
- Problem Solving e.g Adversarial Search
- Knowledge representation
- Probabilistic reasoning
Machine Learning foundation
- Supervised Learning
- Unsupervised Learning – SVM
- Decision trees – Clustering
- Artificial Neural network
- Practical ML (hands on)
Deep Learning foundation
- Introduction, motivation for deep learning
- Set up Anaconda, Jupyter Notebooks
- Applying Deep learning
- Regression
Neural networks
- Maths refresher, Introduction to NumPy, Tensorflow – introduction
- Introduction to Neural Network : Covers in details Perceptron, Gradient descent, multilayered perceptron, Back propagation
- Build your first neural network
- Model evaluation and validation
- Project : Sentiment Analysis of movie reviews
- Develop a mini version of neural network library like Tensorflow
Convolutional Neural Networks
- Intro to Tensorflow
- Using Cloud computing hardware for computations
- Deep Neural networks
- CNN – theory, how and why does it works ?
- Project : Build an Image classifier
- Image generation
Recurrent Neural Networks
- Introduction to RNN
- Long Short Term memory
- LSTM Cell – detailed walkthrough and implementation
- Build a RNN
- Mini Project : Stock price prediction (self proctored)
- Embeddings and Word2Vec
- Sentiment prediction RNN
- Text summarization – using Keras
- Project : Generate TV Scripts
- Designing a Chatbot
- Buid a language translator
Generative Adversarial Networks (advanced)
- Introduction to GAN
- How GANs work
- Games and Equilibria
- Build and train a GAN
- Project: Generate faces : Build 2 NNs to compete with each other to generate realisitic human faces
