The School of AI

Project for The School of AI

This repository will contain projects/asssignments performed for The School Of AI.

SNo.	Topics	Details
1.	Background & Basics	Machine Learning Intuition, Background & Basics of CNN
2.	Neural Architecture	Exhaustive Insights into the Neural Architecture (In classroom Coding or ICC)
3.	First Neural Networks	Hands-on (ICC) to custom design a DNN
4.	DNN Architecture Search	A session where we go through 9 different steps before we arrive at the final architecture "suitable for our objective"
5.	Batch Normalization & Regularization	In-depth coverage on Batch Normalization techniques and different kind of Regularizations, including noise robustness (ICC)
6.	Advanced Convolutions	Advanced Convolutions & Pooling operations with Code examples and usage(ICC)
7.	Receptive Field	Exhaustive Coverage on Receptive Fields, advancements in Receptive Field, and how RF diverges for different kind of problems
8.	Data Augmentation Techniques	Advanced Image Augmentation Techniques, benchmarks against different techniques and ICC
9.	Kernel/Channel Visualization	The most powerful debugging tool at your disposal! (ICC)
10.	Advanced Training Concepts	Advanced concepts on training, including LR, Momentum, Learning Rate Finder,
11.	SuperConvergence	Advanced topics cover to understand and execute Super Convergence
12.	ResNet Part 1	Understanding ResNet end to end (ICC)
13.	ResNext Part 2	Understanding ResNet V2, V3 and ResNext (ICC)
14.	Inception Network	Understanding Inception Networks (ICC)
15.	DenseNet	Understanding DenseNet and it's applications (ICC)
16.	MegaProject	Training ImageNet from scratch with Super Convergence close to StateOfAccuracy
17.	Small DNNs & their advantages Part 1	Building SqueezeNet & MobileNet from scratch. Includes Kernel Reduction, Channel Reduction, Evenly Spaced Downsampling, Cardinality, Shuffle Operation
18.	Small DNNs & their advantages Part 2	Evenly Spaced Downsampling, Cardinality, Shuffle Operation, Distillation & Compression
19.	Transfer Learning	Transfer Learning and approaches. (ICC)
20.	YOLO v2	YOLO V2 Architecture and Design Decisions
21.	YOLO V2 Training	Training YOLO V2 on a custom dataset (with Transfer Learning)
22.	Face Recognition	Building a Face Recognition Model from scratch with advanced Loss functions. ICC
23.	FR using Siamese Network	Building an FR model using Siamese Network. ICC
24.	Zero & One-shot learning	Building a DNN to detect an unseen or never-trained-on object! ICC
25.	UNET	Understanding UNET and it's state of art implementations (image segmentation, etc) ICC
26.	eNAS	How to train a neural network to write a state-of-art neural network
27.	Encoder Decoder Architecture	Representation Learning, Sequence to Sequence Mapping and ICC
28.	GAN & Style Transfer	Generative Adversarial Network and many approaches for the same (DCGAN, CycleGAN). Mode Collapse, Non-convergence and ICC
29.	Variational Autoencoders	Latent Representations using Variational Autoencoders. ICC
30.	Word2Vec & Neural Word Embeddings	Using Word2Vec, ELMO, BERT, GPT-2, Glove & Doc2Vec. ICC
31.	RNN	RNN Basics, advances and drawbacks. Visualizing memorizations in RNNs
32.	LSTM & GRU	The intuition behind LSTM and GRUs. ICC
33.	Attention Mechanism & Memory Networks	Attention & augmented RNNs. Why "Attention"? Memory Networks and ICC
34.	Reinforcement Learning Basics	Background, Intuition, and roadmap
35.	RL Common Approaches	Building various deep learning agents including DQN and A3C (ICC)
36.	OpenGym & RL Basics	OpenAI GYM, and implementation of Q Learning (ICC)
37.	Policy Gradients	Policy Gradient Methods, Continuous Action Spaces, and solving several OpenGym problems (ICC)
38.	Deep Q-Learning	Deep Q Learning, Replay Memory, Partially Observable MDPs and ICC
39.	A3C in depth	A3C in depth and implementation (ICC)
40.	AlphaZero	Training an AlphaZero model from scratch!

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Successfully Completed the EVA course with flying colors

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