Recently have been looking into some stock market prediction libraries and repositories for our group project for CS7643 Deep Learning at Georgia Tech. Previously I worked on traditional Machine Learning algorithms and Q-Learning algorithm from Reinforcement Learning for CS7646: Machine Learning for Trading commonly known as the “ML4T course”. My codes for ML4T is locked […]

# Tag: Deep Learning

Posting some great resources to understand the Transformer architecture for NLP presented in the paper “Attention is All You Need” (Vaswani et al. 2017). This website by J Al-Ammar is excellent The next best resource is this annotated implementation of Transformer in PyTorch from Harvard University Second, read this article called “Attention! Attention!” by Lilian […]

Image Classification: Class distribution per image Semantic Segmentation: Class distribution per pixel Instance Segmentation: Class distribution per pixel with unique ID Object Detection: List of bounding boxes with Class distribution per box

Convolution layer for CNN is explained in simple words

To differentiate the loss function in a Neural Network, there are four options Manual differentiation: It is labor intensive and often it is hard to calculate the closed form solution especially for complex function Symbolic differentiation: Like manual, it is also hard for complex function Numerical differentiation: Can handle complex function but may cause numerical […]