Artificial Intelligence (AI ) and Deep Learning Advance Training

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