My codes for fast.ai's Practical Deep Learning for Coders Part 2 course
-
Updated
May 17, 2017 - Jupyter Notebook
My codes for fast.ai's Practical Deep Learning for Coders Part 2 course
Utilities and helper functions for Pytorch and FastAI deep learning libraries
Building a sentiment classifier that takes movie review text and output rating from 1 to 10 using Word2Vec, bidirectional LSTM, AWD LSTM and more.
Indian car classifier using fastai
WebApp to identify if it is a cheetah, a leopard, or a jaguar by looking at their images
An API for identifying double bass, cello, viola, and violin
Training a model using UNET and ResNet34 to do image segmentation on street images
My Jupyter Notebooks for working through the lessons in fast.ai's Practical Deep Learning for Coders course.
Code written during the EY NextWave Data Science Competition 2019
Book Recommendation based on Collaborative Filtering (using FastAI )and Content Based Recommendation
In this repository, I have created a model which recognizes Happy Face, Sad Face, Surprise Face, Angry Face and Laughing Face using Python, Fastai API and Convolutional Neural Networks.
This project predicts the weather condition from the image which is implemented with the help of Fast.ai library based on Pytorch
2020 Winter Peoplespace Internship Program
Simple project to run deep learning inferencing on AWS Lambda to distinguish donuts from bagels and vadas (South Indian savory dish).
Add a description, image, and links to the fastai topic page so that developers can more easily learn about it.
To associate your repository with the fastai topic, visit your repo's landing page and select "manage topics."