bhaveshwadhwani

a-PyTorch-Tutorial-to-Image-Captioning

Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning

AppliedAI

Course work of Applied AI course

Bert-Model-Transfer-Learning

Whole step by step for transfer learning and fine tuning bert models for task like Q-A, Classifications and others.

CarND-Behavioral-Cloning-P3

Cloning Human behavior for self driving car on simulator track . End to End driving model .

Human-Activity-Recognition

Classifying the physical activities performed by a user based on accelerometer and gyroscope sensor data collected by a smartphone in the user’s pocket. The activities to be classified are: Standing, Sitting, Stairsup, StairsDown, Walking and Cycling.

MachineTranslation

MachineTranslation Project in keras Tensorflow

NYC-Taxi-demand-prediction

A Machine Learning Case Study to predict the Demand of Yellow Taxi in New York City at given location and at a given time interval.

Personalized-Cancer-diagnosis

Diagnosing Genetic Mutation based on classical literature.

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

Stackoverflow-Tag-Prediction

A Machine Learning Case Study to predict the tags for a question on Stackoverflow. This is a Multi-label classification where a data point can belong to more than one class

bhaveshwadhwani/Bert-Model-Transfer-Learning

Whole step by step for transfer learning and fine tuning bert models for task like Q-A, Classifications and others.

bhaveshwadhwani/MachineTranslation

MachineTranslation Project in keras Tensorflow

bhaveshwadhwani/Advanced-NLP

Some Advanced NLP techniques and practices

bhaveshwadhwani/Data-engineering-Working-with-relational-non-relational-db

bhaveshwadhwani/Deep-Learning

Deep Learning specialization by deeplearning.ai

bhaveshwadhwani/DeepLearningFlappyBird

Flappy Bird hack using Deep Reinforcement Learning (Deep Q-learning).

bhaveshwadhwani/Deploying-a-Sentiment-Analysis-Model-on-Amazon-Sagemaker

Deploying-a-Sentiment-Analysis-Model-on-Amazon-Sagemaker using Pytorch

bhaveshwadhwani/Iris_Flower_Classification

Classification of Iris Flower with EDA

bhaveshwadhwani/Plant-Disease-Classification

bhaveshwadhwani/predicting_bike_sharing_patterns

predicting_bike_sharing_patterns

bhaveshwadhwani/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

bhaveshwadhwani/Sentiment_analysis_deep_learning

Sentiment analysis using deep learning Keras

bhaveshwadhwani/Automatic-License-Plate-Recognition

bhaveshwadhwani/bhaveshwadhwani.github.io

My Portfolio Website

bhaveshwadhwani/Carla-RL

Reinforcement Learning codebase for self-driving car in Carla

bhaveshwadhwani/Data-engineering-Norm-Denorm-concept

bhaveshwadhwani/Elastic-Search-Medicine-Search-Engine

An end to end strategy to make a Medicine Search Engine using Elastic Search from scratch in Python.

bhaveshwadhwani/FaceRec

A Deep Learning project for face recognition from video input

bhaveshwadhwani/Facial-Expression-Recognition-Challenge

bhaveshwadhwani/fastbook

Draft of the fastai book

bhaveshwadhwani/google_cloud_tutorials

bhaveshwadhwani/mlops-on-gcp

bhaveshwadhwani/nd00333_AZMLND_Optimizing_a_Pipeline_in_Azure-Starter_Files

bhaveshwadhwani/NLP-Word_embeddings

This repo contains ways to encode words and sentences to vectors using popular methods

bhaveshwadhwani/outputs-code

bhaveshwadhwani/Practical-Deep-Learning-Book

Official code repo for the O'Reilly Book - Practical Deep Learning for Cloud, Mobile & Edge

bhaveshwadhwani/pyimagesearch

Repository for PyImageSearch Projects: https://www.pyimagesearch.com/

bhaveshwadhwani/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.

bhaveshwadhwani/tensorrt_demos

Some examples demonstrating how to optimize caffe/tensorflow/darknet models with TensorRT and run real-time inferencing with the optimized TensorRT engines

bhaveshwadhwani/training-data-analyst

Labs and demos for courses for GCP Training (http://cloud.google.com/training).