ninja3697's Stars
MeghaJakhotia/AlgorithmsPart-I-Coursera
My programming assignments to the Algorithms, Part I course by Kevin Wayne and Robert Sedgewick of the Princeton University.
jasrodis/coursera-algorithms-part1
📖Coursera Princeton Algorithms Part 1
ossu/data-science
📊 Path to a free self-taught education in Data Science!
ossu/computer-science
🎓 Path to a free self-taught education in Computer Science!
microsoft/vscode-extension-samples
Sample code illustrating the VS Code extension API.
CRED-CLUB/neopop-web
NeoPOP components library based on CRED's design system
poteto/hiring-without-whiteboards
⭐️ Companies that don't have a broken hiring process
enaqx/awesome-react
A collection of awesome things regarding React ecosystem
knowledgedefinednetworking/a-deep-rl-approach-for-sdn-routing-optimization
A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization
fchollet/deep-learning-models
Keras code and weights files for popular deep learning models.
Shobhit20/Image-Captioning
Image Captioning: Implementing the Neural Image Caption Generator with python
Igglybuff/awesome-piracy
A curated list of awesome warez and piracy links
aikho/awesome-feature-engineering
A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning
anujdutt9/Feature-Selection-for-Machine-Learning
Methods with examples for Feature Selection during Pre-processing in Machine Learning.
jina-ai/clip-as-service
🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP
keras-team/keras
Deep Learning for humans
ratschlab/RGAN
Recurrent (conditional) generative adversarial networks for generating real-valued time series data.
huseinzol05/Stock-Prediction-Models
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
mbadry1/DeepLearning.ai-Summary
This repository contains my personal notes and summaries on DeepLearning.ai specialization courses. I've enjoyed every little bit of the course hope you enjoy my notes too.
autonomio/talos
Hyperparameter Experiments with TensorFlow and Keras
borisbanushev/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.