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Description
Description
We designed this course in order to provide a deep dive into three aspects of ML:
Theory
Practical
Real world application
We will cover theory in the class and corresponding implementation of the topics in practical sessions. There will be a course group project that will cover real-world applications of AI.
Course Content
Feed Forward Networks (Forward Propagation): [PreClass Reading + Slides] [Lab: Intro to PyTorch + FFN construction]
Feed Forward Networks (Forward Propagation): [PreClass Reading + Slides] [Lab: Intro to PyTorch + FFN construction]
Bias Vs Variance + Optimizers: [Slides] [Lab: Activation Functions, Optimizers and Normalization + Train-Test Split]
Bias Vs Variance + Optimizers: [Slides] [Lab: Activation Functions, Optimizers and Normalization + Train-Test Split]
Seq2Seq Processing: [Slides] [Lab: LSTM Seq2Seq Model] [Dataset]
Seq2Seq Processing: [Slides] [Lab: LSTM Seq2Seq Model] [Dataset]
Transformer Encoder: [Slides] [Lab: Self-Attention, PE + Encoder] [Dataset]
Transformer Encoder: [Slides] [Lab: Self-Attention, PE + Encoder] [Dataset]
Transformer Decoder: [Slides] [Lab: Decoder + Full Architecture] [Dataset]
Transformer Decoder: [Slides] [Lab: Decoder + Full Architecture] [Dataset]
Projects
Coming up soon ......
Course Team
Sukrit Gupta, Instructor
Agaaz Bansal
Amit Kumar
Nitin Kumar
Devansh Arora
Maninder Kaur
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