Foundation Courses

• Basics of Python

• Basics of Deep Learning Library: PyTorch

• Essentials of Vector Calculus and Linear Algebra for Machine Learning

• Conceptual Fundamentals of Machine Learning

Curriculum (Emphasizing on Hands-on)

• Perceptrons and Back propagation

• Ingredients of Deep Learning: Gradient Descent, Batch Normalization, Regularization, Dropout

• Convolutional Neural Networks (CNN)

• Convolutional Autoencoders

• CNN for Object Classification, Detection, and Segmentation

• Recurrent Neural Network, LSTM, Word Embedding

• Attention Models and Transformer (BERT and Visual Transformer)

• Deep Generative Models (GAN and VAE)

• Weakly Supervised Deep Learning

• Self Supervised Learning

• Meta-Learning and Few-Shot Learning

• Geometric Deep Learning

• Deep Reinforcement Learning

• Video Processing