Instructor: Mayank Singh: (office: AB-4/304, email: singh.mayank@iitgn.ac.in)
Class Timings: M and F slot (AB7/207, Thursday Friday 1530-1650)
Tutorial: J1 slot (AB7/206, Tuesday 1400-1520)
QA sessions: Email me to book an appointment for discussing any doubts, clarifications, and concepts.
TAs:
Shruti Singh (singh_shruti@iitgn.ac.in)
V P Shivasankaran (vp.shivasan@iitgn.ac.in)
Dwip Dalal (dwip.dalal@iitgn.ac.in)
Data Structures and Algorithms-I [ES 242 or equivalent]
Introduction to Deep learning
Basics of Linear Algebra
Generative Adversarial Networks
Tips and Tricks to enhance the Performance of Deep Neural Network implementations.
0. Introduction and logistics of the course [Slides]
Introduction to Deep Learning [Slides]
Basics of Linear Algebra [Slides, Coding practice]
Probability and Information Theory [Slides]
Basics of ML [Slides]
Regularization for DL [Notes]
Convolution Networks [Notes]
RNNs [Notes]
Attention Mechanism and Transformers [Slides]
BERT and GPT [Notes]
Tokenization Algorithms [Notes]
Tutorials:
1: Numpy, Pandas, How to enable GPU environment [Colab]
2. PyTorch tensors, Linear algebra [Colab]
3. Probability [Colab]
4. PyTorch Datasets and Dataloaders [Colab]
5. Feed Forward Networks [Colab]
6. Optimization, Gradients, and Weight Visualization [Colab]
7. Convolutional neural networks [Colab]
8. Image Generation using GANs [Colab]
4 Assignments: 50% [3x10% + 1x20%] (Note: You shall get a week's time to complete the assignment. )
A0: Feedforward NNs and MLPs [Link]
A1: Computer Vision Architectures and Benchmarks [Link]
A2: NLP Architectures [Link]
A3: Prompt Engineering [Link]
1 Exam: 30% (This includes everything covered before Feb 04, 2023)
2 Surprise quizzes: 20% [10% each]
Goodfellow, Ian, Yoshua Bengio, Aaron Courville, and Yoshua Bengio. Deep learning. Cambridge: MIT press, 2016. https://www.deeplearningbook.org/
Glassner, Andrew. Deep Learning: A Visual Approach. United States: No Starch Press, 2021.
Glassner, Andrew. Deep Learning: From Basics to Practice. Volume 1 and 2. 2021.
Zhang, Aston, Zachary C. Lipton, Mu Li, and Alexander J. Smola. Dive into Deep Learning. 2020. https://d2l.ai/
Krohn, Jon., Bassens, Aglaé., Beyleveld, Grant. Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence. N.p.: Pearson Education, 2019.
Nielsen, Michael A. Neural networks and deep learning. Vol. 25. San Francisco, CA: Determination press, 2015. http://neuralnetworksanddeeplearning.com/