Bhavesh Wadhwani's repositories
Bert-Model-Transfer-Learning
Whole step by step for transfer learning and fine tuning bert models for task like Q-A, Classifications and others.
MachineTranslation
MachineTranslation Project in keras Tensorflow
Advanced-NLP
Some Advanced NLP techniques and practices
Deep-Learning
Deep Learning specialization by deeplearning.ai
DeepLearningFlappyBird
Flappy Bird hack using Deep Reinforcement Learning (Deep Q-learning).
Deploying-a-Sentiment-Analysis-Model-on-Amazon-Sagemaker
Deploying-a-Sentiment-Analysis-Model-on-Amazon-Sagemaker using Pytorch
Iris_Flower_Classification
Classification of Iris Flower with EDA
predicting_bike_sharing_patterns
predicting_bike_sharing_patterns
Quora-Question-Pair-Similarity
Identify which questions asked on Quora are duplicates of questions that have already been asked.This could be useful to instantly provide answers to questions that have already been answered,predict whether a pair of questions are duplicates or not
Sentiment_analysis_deep_learning
Sentiment analysis using deep learning Keras
bhaveshwadhwani.github.io
My Portfolio Website
Carla-RL
Reinforcement Learning codebase for self-driving car in Carla
Elastic-Search-Medicine-Search-Engine
An end to end strategy to make a Medicine Search Engine using Elastic Search from scratch in Python.
FaceRec
A Deep Learning project for face recognition from video input
NLP-Word_embeddings
This repo contains ways to encode words and sentences to vectors using popular methods
Practical-Deep-Learning-Book
Official code repo for the O'Reilly Book - Practical Deep Learning for Cloud, Mobile & Edge
pyimagesearch
Repository for PyImageSearch Projects: https://www.pyimagesearch.com/
Quora-question-pair-similarity-1
Quora has given an (almost) real-world dataset of question pairs, with the label of is_duplicate along with every question pair. The objective was to minimize the logloss of predictions on duplicacy in the testing dataset. Given a pair of questions q1 and q2, train a model that learns the function: f(q1, q2) → 0 or 1 where 1 represents that q1 and q2 have the same intent and 0 otherwise.
tensorrt_demos
Some examples demonstrating how to optimize caffe/tensorflow/darknet models with TensorRT and run real-time inferencing with the optimized TensorRT engines
training-data-analyst
Labs and demos for courses for GCP Training (http://cloud.google.com/training).