KushajveerSingh / deep_learning

Collection of my work related to deep learning in a single place.

Deep Learning

Collection of my work related to deep learning in a single place.

https://kushajveersingh.github.io/blog/

Repository Structure

1. graph_machine_learning

   1. what_can_neural_networks_reason_about - Implementation of paper What can Neural Networks Reason About. Check my blog post for the paper summary. Results from one of the experiments presented in the paper are reproduced. twitter-card

2. projects

   1. Semi-supervised parking lot detection -> My submission for Techgig Code Gladiators 2019 AI theme competition that won 1st place at the final.
   2. Waste Seggregation using trashnet -> Contains the code to train models for trashnet and then export them using ONNX. It was part of a bigger project where we ran these models on Rasberry Pi, which controlled wooden planks to classify the waste into different categories (code for rasberry pi not included here).
   3. Human 3D Reconstruction -> Uses Insetgan and Pifuhd models to generate 3D full body images of people.

3. paper_implementations

   1. Photorealistic Style Transfer - Implementation of High Resolution Network for Photorealistic Style Transfer
   2. SPADE-PyTorch - Implementation of Semantic Image Synthesis with Spatially-Adaptive Normalization (SPADE)
   3. Weight Standardization - Implementation of Weight Standardization. Tested using cyclic learning.
   4. Training AlexNet with tips and checks on how to train CNNs - A PyTorch tutorial on how to create an image classifier.
   5. Study of Mish activation function in transfer learning with code and discussion - Implementation of Mish: A Self Regularized Non-Monotonic Neural Activation Function.
   6. Number of bins of a Histogram - Notebook discussing three techniques of choosing the bin size in histograms.
   7. Multi Sample Dropout - Implementation of Multi-Sample Dropout for Accelerated Training and Better Generalization.
   8. Data Augmentation in Computer Vision - Notebook implementing single image data augmentation techniques using just Python.
   9. How to deal with outlier - Notebook discussing ways to deal with outliers.
   10. Leslie N. Smith papers notebook - Jupyter notebook discussing cyclic learning and ways by which we can choose hyperparameter values by looking at valid loss graph.

4. notes - My notes when learning some things.

   • Cython
   • dask
   • matplotlib
   • PIL
   • seaborn
   • vim
   • xarray
   • Python - Data model + James Powell talks

5. random - Random scripts and os setup instructions.

   1. os_setup_instructions - My setup guide for Windows 10 and Ubuntu 20.04 (needs to be updated a bit).
   2. cifar10_data_script.py - To convert CIFAR10 data to numpy array
   3. save_torchvision_models_to_disk -> a quick script to download all pytorch models
   4. unscramble_android_game -> Python script to solve the unscramble android game. Complexity is exponential to generate all the substrings, and a dictionary is used to check for valid words.
   5. download_conference_papers -> Python scripts to download conference papers as PDFs (like CVPR, ICLR, ECCV) with utility functions what can be used to download papers from any conference.
   6. dash_cdc_jhu_visualization -> Python program to download CDC and JHU covid data, and then visualize the discrepencies between the two sources of data using Dash.