I will implement Fastai in each projects present in this repository.

Overview

DEEP LEARNING FOR CODERS WITH FASTAI AND PYTORCH

The repository contains a list of the projects which I have worked on while reading the book Deep Learning For Coders with Fastai and PyTorch.

📚 NOTEBOOKS:

1. INTRODUCTION

  • The Introduction notebook is a comprehensive notebook as it contains a list of projects such as Cat and Dog Classification, Semantic Segmentation, Sentiment Classification, Tabular Classification and Recommendation System.

2. MODEL PRODUCTION

  • The BearDetector notebook contains all the dependencies for a complete Image Classification project.

3. TRAINING A CLASSIFIER

  • The DigitClassifier notebook contains all the dependencies required for Image Classification project from scratch.

4. IMAGE CLASSIFICATION

  • The Image Classification notebook contains all the dependencies for Image Classification such as getting image data ready for modeling i.e presizing and data block summary and for fitting the model i.e learning rate finder, unfreezing, discriminative learning rates, setting the number of epochs and using deeper architectures. It has explanations of cross entropy loss function as well.

5. MULTILABEL CLASSIFICATION AND REGRESSION

  • The Multilabel Classification notebook contains all the dependencies required to understand Multilabel Classification. It contains the explanations of initializing DataBlock and DataLoaders. The Regression notebook contains all the dependencies required to understand Image Regression.

6. ADVANCED CLASSIFICATION

  • The Imagenette Classification notebook contains all the dependencies required to train a state of art machine learning model in computer vision whether from scratch or using transfer learning. It contains explanations and implementation of Normalization, Progressive Resizing, Test Time Augmentation, Mixup Augmentation and Label Smoothing.

7. COLLABORATIVE FILTERING

  • The Collaborative Filtering notebook contains all the dependencies required to build a Recommendation System. It presents how gradient descent can learn intrinsic factors or biases about items from a history of ratings which then gives information about the data.

8. TABULAR MODELING

  • The Tabular Model notebook contains all the dependencies required for Tabular Modeling. It presents the detailed explanations of two approaches to Tabular Modeling: Decision Tree Ensembles and Neural Networks.

9. NATURAL LANGUAGE PROCESSING

  • The NLP notebook contains all the dependencies required build Language Model that can generate texts and a Classifier Model that determines whether a review is positive or negative. It presents the state of art Classifier Model which is build using a pretrained language model and fine tuned it to the corpus of task. Then the Encoder model is used for classification.

10. DATA MUNGING

  • The DataMunging notebook contains all the dependencies required to implement mid level API of Fast.ai in Natural Language Processing and Computer Vision which provides greater flexibility to apply transformations on data items.

11. LANGUAGE MODEL FROM SCRATCH

  • The LanguageModel notebook contains all the dependencies that is inside AWD-LSTM architecture for Text Classification. It presents the implementation of Language Model using simple Linear Model, Recurrent Neural Network, Long Short Term Memory, Dropout Regularization and Activation Regularization.

12. CONVOLUTIONAL NEURAL NETWORK

  • The CNN notebook contains all the dependencies required to understand Convolutional Neural Networks. Convolutions are just a type of matrix multiplication with two constraints on the weight matrix: some elements are always zero and some elements are tied or forced to always have the same value.

13. RESIDUAL NETWORKS

  • The ResNets notebook contains all the dependencies required to understand the implementation of skip connections which allow deeper models to be trained. ResNet is the pretrained model when using Transfer Learning.

14. ARCHITECTURE DETAILS

  • The Architecture Details notebook contains all the dependencies required to create a complete state of art computer vision models. It presents some aspects of natural language processing as well.

15. TRAINING PROCESS

  • The Training notebook contains all the dependencies required to create a training loop and explored variants of Stochastic Gradient Descent.

16. NEURAL NETWORK FOUNDATIONS

  • The Neural Foundations notebook contains all the dependencies required to understand the foundations of deep learning, begining with matrix multiplication and moving on to implementing the forward and backward passes of a neural net from scratch.

17. CNN INTERPRETATION WITH CAM

  • The CNN Interpretation notebook presents the implementation of Class Activation Maps in model interpretation. Class activation maps give insights into why a model predicted a certain result by showing the areas of images that were most responsible for a given prediction.

18. FASTAI LEARNER FROM SCRATCH

  • The Fastai Learner notebook contains all the dependencies to understand the key concepts of Fastai.

19. CHEST X-RAYS CLASSIFICATION

20. TRANSFORMERS MODEL

You might also like...
Finding an Unsupervised Image Segmenter in each of your Deep Generative Models

Finding an Unsupervised Image Segmenter in each of your Deep Generative Models Description Recent research has shown that numerous human-interpretable

This solves the autonomous driving issue which is supported by deep learning technology. Given a video, it splits into images and predicts the angle of turning for each frame.
This solves the autonomous driving issue which is supported by deep learning technology. Given a video, it splits into images and predicts the angle of turning for each frame.

Self Driving Car An autonomous car (also known as a driverless car, self-driving car, and robotic car) is a vehicle that is capable of sensing its env

Feed forward VQGAN-CLIP model, where the goal is to eliminate the need for optimizing the latent space of VQGAN for each input prompt
Feed forward VQGAN-CLIP model, where the goal is to eliminate the need for optimizing the latent space of VQGAN for each input prompt

Feed forward VQGAN-CLIP model, where the goal is to eliminate the need for optimizing the latent space of VQGAN for each input prompt. This is done by

Hippocampal segmentation using the  UNet network for each axis
Hippocampal segmentation using the UNet network for each axis

Hipposeg Hippocampal segmentation using the UNet network for each axis, inspired by https://github.com/MICLab-Unicamp/e2dhipseg Red: False Positive Gr

The LaTeX and Python code for generating the paper, experiments' results and visualizations reported in each paper is available (whenever possible) in the paper's directory
The LaTeX and Python code for generating the paper, experiments' results and visualizations reported in each paper is available (whenever possible) in the paper's directory

This repository contains the software implementation of most algorithms used or developed in my research. The LaTeX and Python code for generating the

Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel order of RGB and BGR. Simple Channel Converter for ONNX.
Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel order of RGB and BGR. Simple Channel Converter for ONNX.

scc4onnx Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel

Template repository for managing machine learning research projects built with PyTorch-Lightning

Tutorial Repository with a minimal example for showing how to deploy training across various compute infrastructure.

Implement face detection, and age and gender classification, and emotion classification.
Implement face detection, and age and gender classification, and emotion classification.

YOLO Keras Face Detection Implement Face detection, and Age and Gender Classification, and Emotion Classification. (image from wider face dataset) Ove

offical implement of our Lifelong Person Re-Identification via Adaptive Knowledge Accumulation in CVPR2021
offical implement of our Lifelong Person Re-Identification via Adaptive Knowledge Accumulation in CVPR2021

LifelongReID Offical implementation of our Lifelong Person Re-Identification via Adaptive Knowledge Accumulation in CVPR2021 by Nan Pu, Wei Chen, Yu L

Owner
Thinam Tamang
Machine Learning and Deep Learning
Thinam Tamang
The fastai deep learning library

Welcome to fastai fastai simplifies training fast and accurate neural nets using modern best practices Important: This documentation covers fastai v2,

fast.ai 20.4k Feb 12, 2021
tsai is an open-source deep learning package built on top of Pytorch & fastai focused on state-of-the-art techniques for time series classification, regression and forecasting.

Time series Timeseries Deep Learning Pytorch fastai - State-of-the-art Deep Learning with Time Series and Sequences in Pytorch / fastai

timeseriesAI 2.8k Dec 7, 2022
fastgradio is a python library to quickly build and share gradio interfaces of your trained fastai models.

fastgradio is a python library to quickly build and share gradio interfaces of your trained fastai models.

Ali Abdalla 32 May 15, 2022
An Agnostic Computer Vision Framework - Pluggable to any Training Library: Fastai, Pytorch-Lightning with more to come

IceVision is the first agnostic computer vision framework to offer a curated collection with hundreds of high-quality pre-trained models from torchvision, MMLabs, and soon Pytorch Image Models. It orchestrates the end-to-end deep learning workflow allowing to train networks with easy-to-use robust high-performance libraries such as Pytorch-Lightning and Fastai

airctic 782 Nov 29, 2022
We present a framework for training multi-modal deep learning models on unlabelled video data by forcing the network to learn invariances to transformations applied to both the audio and video streams.

Multi-Modal Self-Supervision using GDT and StiCa This is an official pytorch implementation of papers: Multi-modal Self-Supervision from Generalized D

Facebook Research 40 Oct 15, 2022
PPO is a very popular Reinforcement Learning algorithm at present.

PPO is a very popular Reinforcement Learning algorithm at present. OpenAI takes PPO as the current baseline algorithm. We use the PPO algorithm to train a policy to give the best action in any situation.

Rosefintech 11 Aug 23, 2021
Codes for realizing theories learned from Data Mining, Machine Learning, Deep Learning without using the present Python packages.

Codes-for-Algorithms Codes for realizing theories learned from Data Mining, Machine Learning, Deep Learning without using the present Python packages.

Tracy (Shengmin) Tao 1 Apr 12, 2022
Flybirds - BDD-driven natural language automated testing framework, present by Trip Flight

Flybird | English Version 行为驱动开发(Behavior-driven development,缩写BDD),是一种软件过程的思想或者

Ctrip, Inc. 700 Dec 6, 2022
A compendium of useful, interesting, inspirational usage of pandas functions, each example will be an ipynb file

Pandas_by_examples A compendium of useful/interesting/inspirational usage of pandas functions, each example will be an ipynb file What is this reposit

Guangyuan(Frank) Li 32 Nov 20, 2022
Pytorch implementation of Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization https://arxiv.org/abs/2008.11646

[TCSVT] Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization LPN [Paper] NEWs Prerequisites Python 3.6 GPU Memory >= 8G Numpy > 1.

null 44 Oct 20, 2022