Deep Learning, Computer Vision, and Natural Language Processing!
Curriculum
Welcome
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Gamification
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Milestone 0 - Recap & Pre-requisites
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- 1 - Calculus (25:07)
- 2 - Basics of Gradient Descent (62:56)
- 3 - Coding Gradient Descent and Understanding Learning Rates (33:46)
- 4 - Versions of GD, Minimas, Initialisation, Feature Scaling, Learning Rate (43:56)
- 5 - Why Tensorflow? (31:34)
- 6 - Project: Intro to Object Segmentation (52:52)
- 7 - Project: Preprocessing (30:56)
- 8 - Project: Model Building and Evaluation (63:23)
- Refresher Quiz (Non-graded)
Milestone 1 - Intro to Tensorflow & Professional Workflows
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Milestone 2 - Your First Neural Network!
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- Day 1 (a) - Intro to DL, Perceptron, MLP (34:50)
- Day 1 (b) - Deep Learning v/s Classical ML (28:44)
- Day 2 - How NN learn, Notation, Forward Pass (45:51)
- Day 3 - Trainable Parameters, Backpropagation Derivation, and Memoization (40:36)
- Day 4 - Let's code it up! (40:49)
- Day 5 - What are the correct number of epochs? (30:10)
- Day 6 & 7 - Quiz 2
- Day 8 - Intro to Activation Functions & The Important of Non-linearity (41:02)
- Day 9 (a) - Different Activation Functions with properties (33:58)
- Day 9 (b) - Dying ReLU: what, why, and the solution! (23:33)
- Day 9 (c) - Variants of ReLU, SoftMax (16:17)
- Day 10 - Loss Functions (56:58)
- Day 11 - Recap of Information Theory (79:00)
Milestone 3 - Practical Considerations (Phase A)
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- Day 1 - What is the correct batch size? (43:22)
- Day 2 - Vanishing Gradient: Problem (35:31)
- Day 3 - (a) Vanishing Gradient: Detection & Solution (14:47)
- Day 3 - (b) Exploding Gradient Problem & Gradient Clipping (20:18)
- Day 4 - (a) Weight Initialisation (22:31)
- Day 4 - (b) Code: Activation Fns, Vanishing/Exploding Grad, Weight Init (29:29)
- Day 5 - Depth v/s Width (18:46)
Milestone 4 - Dealing with Images
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- Day 1 - Color Spaces and Types of Images (47:41)
- Day 2 - Image Manipulation with Filters (53:55)
- Day 3 - OpenCV: Color Spaces, Equivalisation, and Thresholding (45:12)
- Day 4 - OpenCV: Image Smoothing, Morphological Ops, Edge Detection (26:46)
- Day 5 - Quiz 3
- Day 6 - How CNNs were built? (31:20)
- Day 7 (a) - Convolution, Feature Maps, and Output Sizes (35:54)
- Day 7 (b) - Parameter Sharing, Num of Params, Receptive Field (23:58)
- Day 8 (a) - Visualising the feature maps (26:30)
- Day 8 (b) - Let's play pool! (32:45)
- Day 9 - Coding a CNN, CNN v/s ANN on Cifar-10, Softmax Stability, Evolution of CNNs (LeNet, AlexNet, etc.) (61:54)
- Day 10 & 11 - Model Building: Pre-existing, Pre-training, Transfer, Finetune, Auxilliary, Unsupervised Pre-training (75:39)
- Day 12 & 13 - Quiz 4: Build a CNN on the Cifar-100 dataset
- Day 14 - Data Augmentation (40:24)
- Day 15 - Overfitting and Dropout (62:14)
- Day 16 (a) - Implementing Dropout (8:29)
- Day 16 (b) - Regularisation in Neural Networks (12:07)
- Day 17 - ResNet, Residual Blocks, and Skip Connections (34:26)
- Day 18 - Encoder-Decoder, Convolution Transpose, U-net (42:49)
Milestone 5 - Hackathon
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Milestone 6 - Special Neural Networks
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Further Milestones
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Support Sessions
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Extras
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Reading Room & Labs
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- Seminal Papers List
- 1.1: Introduction to Research in AI (111:53)
- 1.2: Submit Your Literature Survey!
- 2.1: 9 June - Live Session on Branding Beyond Github Profiles! (28:11)
- 2.2: Submit Your Medium Blog!
- 3.1 - Lab Session: Let's Build A Virtual TryOn! (Model Designs, Inpainting, Running GitHub Repos, Custom Data Loaders & Losses) (207:54)