Introduction to Deep Learning

● Built a fully functional autograd driven DL framework with implementation for functional modules like Conv1D, Conv2D, LSTM, GRU and optimization algorithms including Adam, SGD and RMSprop.
● Trained MobileNet, ConvNext Net and ResNet models from scratch for classification of person’s ID based on dataset of face images and performed face verification using cosine similarity metric.
● Designed an end to end system for speech to text transcription using a combination of Recurrent Neural Networks (RNNs) and Attention models, such that the system was able to transcribe a given speech utterance to its corresponding transcript. The architecture for the project was inspired from Listen Attend and Spell Paper.

Learning Outcomes:

1. Neural Networks As Universal Approximators
2. Convolutional Neural Networks
3. Time Series, Recurrent Networks and LSTMs
4. Language Models and Sequence To Sequence Prediction
5. Attention and Transformer
6. GANs, Autoencoders and VAEs

Programming Language: Pytorch, Python