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stationarizer

Smart, automatic detection and stationarization of non-stationary time series data.

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.

swarmopt

Swarm intelligence optimizer

swarmpackagepy

Library of swarm optimization algorithms.

system-design-primer

Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.

t81_558_deep_learning

Washington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks

tabsurvey

Experiments on Tabular Data Models

targer

BiLSTM-CNN-CRF tagger

text-classification

An example on how to train supervised classifiers for multi-label text classification using sklearn pipelines

tf2-rl

Reinforcement learning algorithms implemented for Tensorflow 2.0+ [DQN, DDPG, AE-DDPG, SAC, PPO, Primal-Dual DDPG]

tf2.0_reinforcement_learning

Basic reinforcement learning implementation with tensorflow version 2.0

tfvaegan

Official repository with Pytorch implementation for "Latent Embedding Feedback and Discriminative Features for Zero-Shot Classification" (ECCV 2020)

the-ultimate-flask-course

The Ultimate Flask Course, published by Packt

tianshou

An elegant, flexible, and superfast PyTorch deep reinforcement learning platform.

time-series-forecasting-of-amazon-stock-prices-using-neural-networks-lstm-and-gan-

Project analyzes Amazon Stock data using Python. Feature Extraction is performed and ARIMA and Fourier series models are made. LSTM is used with multiple features to predict stock prices and then sentimental analysis is performed using news and reddit sentiments. GANs are used to predict stock data too where Amazon data is taken from an API as Generator and CNNs are used as discriminator.