Prabhsimran Singh's repositories
trading-bot
Stock Trading Bot using Deep Q-Learning
resnet-classifier
Deep ConvNet Image Classifier based on Residual Network architecture trained on Caltech 101 Object Dataset
translator
PyTorch implementation of Machine Translation using Autoencoder with Attention
lstm-from-scratch
LSTM Network from Scratch in C++
char-level-rnn
Character Level RNN language model in PyTorch
end-to-end-enc-chat
Real-time Chat app with end-to-end encryption using AES-256 and RSA
github-issues-tracker
GitHub Issues Tracker for Public Repositories
pytorch-seq2seq
An open source framework for seq2seq models in PyTorch.
deepspeech.pytorch
Speech Recognition using DeepSpeech2.
pos-tagger
Part-Of-Speech Tagging for NLP systems using Keras
AI-voice-assistant
Python voice command assistant :milky_way:
deep-learning-drizzle
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
descriptor
Image Captioning using CNN Encoder and RNN Decoder
fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
GPV
Repository for our Interspeech2020 general-purpose voice activity detection (GPVAD) paper
hackerearth-codemonk-series
Solutions for HackerEarth CodeMonk Series in C++
kaldi
This is the official location of the Kaldi project.
learn-go-with-tests
Learn Go with test-driven development
lute
Lute is a framework for writing compute DAGs
NeMo
NeMo: a toolkit for conversational AI
neural-networks
Neural Network from Scratch with Python
paper-reading
List of papers we cover during our weekly paper reading session
pskrunner14.github.io
Personal Portfolio Website
Speech-Recognition
Hosted files for DEV287x-Speech Recognition Systems
stockpredictionai
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.