This project demonstrates the use of various pre-trained models for transfer learning in NLP using TensorFlow Hub.

Проект по созданию системы безопасности на предприятии, реализующей детектирование людей и автоматическую проверку наличия касок по изображениям с видеокамер

A plugin for TensorBoard to customize and export Scalar plots

Experimentation & Results of nvidia-modulus-sym implementation using PINNs

Encoder-Decoder Transformer with cross-attention

This repo contains different models and technique used to classify image in MNIST, CIFSR-10, IRIS Datasets.

code I used to learn deep learning

Gaining advanced insights from Git repository history.

this repo contains my learning journey of PyTorch Programming

Deep Reinforcement Learning Projects

Using Machine Learning and Deep Learning to predict the category of outdoor equipment

A Logistic Regression model which predicts and differentiates the diabetic patients from the given Pima Indians Diabetes Database dataset. Hyper parameter tuning is implemented using Tensorflow board visualizations.

Solving the Atari Breakout environment using Stable Baselines

CartPole environment using Stable BaseLines library

Domain: Computer Vision | Deep Learning

To test if CAPTCHA secure login security codes are secure from bot attacks I developed a CNN classifier model in Python using TensorFlow to predict the CAPTCHA codes. Predicted over 40,000 CAPTCHA codes with an accuracy above 98%.

A Pytorch Project with flask API to predict handwritten digits using MNIST Dataset.

An LSTM model to label articles into 5 categories.

A model to classify images of waste products as Organic or Recyclable. Applied Image Augmentation to images and used basic CNN to classify images using Keras. Analysed the performance using Tensorboard. Detected over fitting using metric curves (accuracy) and addressed it using Dropout Regularization.

Create Convolutional Neural Network from scratch with potato disease classification. App will allow farmers to snap a picture of a plant and determine whether the plant has a disease or not.