Aayush Kandpal's repositories
Stock-Prediction-LSTM-model-TESLA-stock
Model to predict the stock prices of TESLA (TSLA) using the data of last 4 years
Artificial-Neural-Networks-for-Detecting-Fraud-
This is a simple ANN with some techniques such as dropout regularisation to predict fraud using bank details
Basic-monte-carlo-Simulation-
A basic monte carlo simulation to analyse the price of a given stock based on normal distribution.
SOM-Credit-Card-Fraud-Detection-
Unsupervised learning (Self Organising Maps ) basic Approach
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.
100-nlp-papers
100 Must-Read NLP Papers
Crop-Condition-Predictor-
Based on the data of about 90000 crops data. Condition of 60000 crops is to be predicted
deep-learning-ann-churn-modeling
ANN Geographic Segmentation Model to tell which of the customers are at highest risk of leaving
Heart-Disease-UCI-Model
Predicting a heart disease accurately based on 14 markers.
pytorch-sentiment-analysis
Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
transformers
🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.
Background-removal-using-deep-learning
This is the code and introduction for how to apply simple deep learning method on background removal.
chefboost
A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4,5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting (GBDT, GBRT, GBM), Random Forest and Adaboost w/categorical features support for Python
jupyter-text2code
A proof-of-concept jupyter extension which converts english queries into relevant python code
lstm
Minimal, clean example of lstm neural network training in python, for learning purposes.
NormalizedNerd
Codes for the videos of my YouTube channel
pytorch-book
PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch：入门与实战》)
pytorch-tutorial
PyTorch Tutorial for Deep Learning Researchers
QuestionBank
A QuestionBank of data science interview Questions
reinforcement-learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
restricted-boltzmann-machines
Restricted Boltzmann Machines in Python.
seq2seq
Sequence to Sequence Learning with Keras
Streamlit_DataScience_Apps
Streamlit Data Science and ML Apps in Python
TensorFlow-Tutorials
TensorFlow Tutorials with YouTube Videos
tensorflow_time_series
This repo contains RNN model for time series prediction of binary digit series
torch-residual-networks
This is a Torch implementation of ["Deep Residual Learning for Image Recognition",Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun](http://arxiv.org/abs/1512.03385) the winners of the 2015 ILSVRC and COCO challenges.
UNET-for-high-resolution-image-segmentation
This is a UNET model for high resolution image segmentation. No cropping was used in the skip connection , hence, we were able to achieve the high resolution outputs. The input images are 3 channel 1024x1024 images. The output is 1 channel 1024x1024 image.