Self-Learning - Books Papers, Courses & more I have to learn soon

Overview

Self-Learning

This repository is intended to be used for personal use, all rights reserved to respective owners, please cite original authors and ask for permissions as specified in any document present here-in

Study Material

Basic

  • Linear Algebra Gilbert Strang
  • Probability & Statistics basics
  • Hands On Machine learning Book
  • Piyush Rai Slides, IIT-K
  • [ ]

Advanced

  • Elements of Statistical Learning Theory
  • Pattern Recognition & Machine Learning .Bishop
  • Deep learning .Goodfellow
  • Reinforcement Learning
  • Time Series
  • [ ]

DeepLearning.Ai

  • Deep Learning Specialization
  • Tensorflow in Practice
  • Tensorflow: Data & Deployment
  • AI for Everyone

YouTube Courses

  • 3Blue1Brown (LA, Calculus, DiffEq, Neural Networks)
  • Advanced Deep & Reinforcement Learning
  • Reinforcement Learning - David Silver

MIT-OCW

  • Linear Algebra
  • Introduction to Probability
  • Matrix Methods in Data Analysis, Signal Processing, and Machine Learning
  • Introduction to Algorithms
  • Design and Analysis of Algorithms

NPTEL

  • Numerical Optimization
  • Pattern Recognition and Neural Networks

Stanford

  • Natural Language Understanding
  • NLP with Deep Learning
  • Deep Learning
  • Reinforcement Learning

Projects

  • Image Classification
  • SISR, CAR, Denoising
  • Sentiment Analysis/Classification
  • Adversarial Machine Learning
  • Style Transfer/Generation
  • Time Series Forecasting
  • Cardinality Estimation
  • [ ]
  • Question Answering
  • Speech Synthesis
  • Text to SQL
  • Audio Source Separation
  • [ ]
  • [ ]
conda update conda
conda create -n py38 python=3.8
conda activate py38
conda install numpy scipy sympy matplotlib seaborn holoviews panel bokeh pandas scikit-learn scikit-image pillow ipython jupyter numba joblib dask dask-ml h2o django flask gevent requests lightgbm catboost nltk imbalanced-learn
pip install --upgrade opencv-python streamlit jupyter_http_over_ws xgboost
pip install --upgrade tensorflow keras-tuner
conda update --all

import tensorflow as tf
tf.config.list_physical_devices('GPU')

jupyter serverextension enable --py jupyter_http_over_ws
jupyter notebook --NotebookApp.allow_origin='https://colab.research.google.com' --port=6006 --NotebookApp.port_retries=0

conda create -n py38 python=3.8 --no-default-packages
conda remove -n py38 --all

conda install -c anaconda-nb-extensions nb_conda
conda install -c anaconda psycopg2

# Teamviewer Not Launching in Ubuntu18.04
systemctl restart teamviewerd

python 

SciPy Stack (Numpy, Matplotlib, Pandas, SymPy & Scipy Included)

https://scipy.org

SEABORN (Powerful pretty plotting library)

https://seaborn.pydata.org

Scikit-Learn (Standard ML and many algorithms implemented)

https://scikit-learn.org/stable/

High-level Neural Network API (Yet customizable)

https://keras.io

Visualising Neural Network Training, Computation graph and a lot

https://www.tensorflow.org/tensorboard

Backend for Keras, Powerful tool for ML/DL & Simulation research

https://www.tensorflow.org

Distributed load balanced data handling (over-system & clusters)

https://dask.org

ML implementation of Most Scikit-learn Algorithms, highly scalable

https://ml.dask.org

Great examples on how to use DASK

https://examples.dask.org

Machine learning, Data processing & more on Nvidia GPU

https://rapids.ai

Building High level data apps with Ease

https://www.streamlit.io

TF projector for visualization with Dimensionality reduction

https://projector.tensorflow.org

Creating VMs (Infra+Platform) over GCP

https://console.cloud.google.com/getting-started

Codelabs provide a Step-wise, learning tutorials, hands-on coding experience. To build a small application OR adding features into existing application

https://codelabs.developers.google.com

Connecting Google colab notebooks to local runtime

https://research.google.com/colaboratory/local-runtimes.html

Connecting Google Colab to Local Runtime

pip install jupyter_http_over_ws

jupyter serverextension enable --py jupyter_http_over_ws

jupyter notebook
--NotebookApp.allow_origin='https://colab.research.google.com'
--port=6006
--NotebookApp.port_retries=0

https://github.com/quantopian/zipline https://github.com/EliteQuant/EliteQuant https://github.com/ashishpatel26/Tools-to-Design-or-Visualize-Architecture-of-Neural-Network

Windows/Linux Utility Software

  • 7-zip
  • Adobe Reader DC
  • Anaconda3
  • AnyDesk
  • AOMEI Partition Wizard
  • CISCO AnyConnect
  • Dev-C++
  • Free Download Manager
  • Git
  • Google Chrome
  • Java SDK
  • MS Office/One-Drive
  • VS Code
  • Mozilla Firefox
  • PostgreSQL
  • PowerISO
  • Putty
  • Samsung Magician
  • Spotify
  • Sublime Text 3
  • TeamViewer
  • Universal ADB driver for Vysor
  • VLC Media Player
  • WinRAR
  • WinSCP

Hobby-Projects

Owner
Achint Chaudhary
Computer Science Masters at Indian Institute of Science, Bangalore
Achint Chaudhary
Fast and simple implementation of RL algorithms, designed to run fully on GPU.

RSL RL Fast and simple implementation of RL algorithms, designed to run fully on GPU. This code is an evolution of rl-pytorch provided with NVIDIA's I

Robotic Systems Lab - Legged Robotics at ETH Zürich 68 Dec 29, 2022
This repository contains demos I made with the Transformers library by HuggingFace.

Transformers-Tutorials Hi there! This repository contains demos I made with the Transformers library by 🤗 HuggingFace. Currently, all of them are imp

3.5k Jan 01, 2023
Codebase for ECCV18 "The Sound of Pixels"

Sound-of-Pixels Codebase for ECCV18 "The Sound of Pixels". *This repository is under construction, but the core parts are already there. Environment T

Hang Zhao 318 Dec 20, 2022
This repository contains notebook implementations of the following Neural Process variants: Conditional Neural Processes (CNPs), Neural Processes (NPs), Attentive Neural Processes (ANPs).

The Neural Process Family This repository contains notebook implementations of the following Neural Process variants: Conditional Neural Processes (CN

DeepMind 892 Dec 28, 2022
Source code for "Pack Together: Entity and Relation Extraction with Levitated Marker"

PL-Marker Source code for Pack Together: Entity and Relation Extraction with Levitated Marker. Quick links Overview Setup Install Dependencies Data Pr

THUNLP 173 Dec 30, 2022
Development of IP code based on VIPs and AADM

Sparse Implicit Processes In this repository we include the two different versions of the SIP code developed for the article Sparse Implicit Processes

1 Aug 22, 2022
Logistic Bandit experiments. Official code for the paper "Jointly Efficient and Optimal Algorithms for Logistic Bandits".

Code for the paper Jointly Efficient and Optimal Algorithms for Logistic Bandits, by Louis Faury, Marc Abeille, Clément Calauzènes and Kwang-Sun Jun.

Faury Louis 1 Jan 22, 2022
Implementation of trRosetta and trDesign for Pytorch, made into a convenient package

trRosetta - Pytorch (wip) Implementation of trRosetta and trDesign for Pytorch, made into a convenient package

Phil Wang 67 Dec 17, 2022
Pytorch Lightning Distributed Accelerators using Ray

Distributed PyTorch Lightning Training on Ray This library adds new PyTorch Lightning plugins for distributed training using the Ray distributed compu

167 Jan 02, 2023
TensorFlow implementation of "Attention is all you need (Transformer)"

[TensorFlow 2] Attention is all you need (Transformer) TensorFlow implementation of "Attention is all you need (Transformer)" Dataset The MNIST datase

YeongHyeon Park 4 Jan 05, 2022
Attention over nodes in Graph Neural Networks using PyTorch (NeurIPS 2019)

Intro This repository contains code to generate data and reproduce experiments from our NeurIPS 2019 paper: Boris Knyazev, Graham W. Taylor, Mohamed R

Boris Knyazev 242 Jan 06, 2023
Simple tools for logging and visualizing, loading and training

TNT TNT is a library providing powerful dataloading, logging and visualization utilities for Python. It is closely integrated with PyTorch and is desi

1.5k Jan 02, 2023
Joint-task Self-supervised Learning for Temporal Correspondence (NeurIPS 2019)

Joint-task Self-supervised Learning for Temporal Correspondence Project | Paper Overview Joint-task Self-supervised Learning for Temporal Corresponden

Sifei Liu 167 Dec 14, 2022
Official PyTorch implementation of Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval.

Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval PyTorch This is the PyTorch implementation of Retrieve in Style: Unsupervised Fa

60 Oct 12, 2022
Code for our method RePRI for Few-Shot Segmentation. Paper at http://arxiv.org/abs/2012.06166

Region Proportion Regularized Inference (RePRI) for Few-Shot Segmentation In this repo, we provide the code for our paper : "Few-Shot Segmentation Wit

Malik Boudiaf 138 Dec 12, 2022
User-friendly bulk RNAseq deconvolution using simulated annealing

Welcome to cellanneal - The user-friendly application for deconvolving omics data sets. cellanneal is an application for deconvolving biological mixtu

11 Dec 16, 2022
Official Tensorflow implementation of "M-LSD: Towards Light-weight and Real-time Line Segment Detection"

M-LSD: Towards Light-weight and Real-time Line Segment Detection Official Tensorflow implementation of "M-LSD: Towards Light-weight and Real-time Line

NAVER/LINE Vision 357 Jan 04, 2023
Calibrate your listeners! Robust communication-based training for pragmatic speakers. Findings of EMNLP 2021.

Calibrate your listeners! Robust communication-based training for pragmatic speakers Rose E. Wang, Julia White, Jesse Mu, Noah D. Goodman Findings of

Rose E. Wang 3 Apr 02, 2022
Distributed Deep learning with Keras & Spark

Elephas: Distributed Deep Learning with Keras & Spark Elephas is an extension of Keras, which allows you to run distributed deep learning models at sc

Max Pumperla 1.6k Jan 05, 2023
Extreme Lightwegith Portrait Segmentation

Extreme Lightwegith Portrait Segmentation Please go to this link to download code Requirements python 3 pytorch = 0.4.1 torchvision==0.2.1 opencv-pyt

HYOJINPARK 59 Dec 16, 2022