mohsin-ashraf / helpful-articles

This repository contains only the links to the helpful articles.

helpful-articles

This repository contains only the links to the helpfull articles.

Python

• Decorator comprehensive guide
  • A very friendly guide for understanding the decorators from scratch to advance level with examples & usecases.

Learning Resources

• Fast.ai
  • Website offering courses in Deep learning, Artificial Intelligence and cloud computing.
• MIT Courses
  • Excellent resource where lectures of MIT on Machine learning, Deep learning and Re-enforcement learning are placed.

Web Scraping

• Selenium chromedriver on ubuntu
• Requests Beyond the basics
  • Comprehensive guide for advanced requests. Resuming downloads or incomplete binary files and how to show the progress bar

Pandas

• Pandas Data Selection & Other Features
  • Medium blog posts collections explaining advanced ways of selecting data using pandas and other advanced features and challenges to learn pandas.
• Pandas for large data
  • It a very good post talking about efficiently working with large dataset using pandas in a memory effiecient way. Shows some tricks to do stuff parallel in pandas and shows examples.
• Setting up Jupyter notebook on remote server
  • A very simple and friendly post for setting up jupyter notebook on remote servers, and using it from local computers.

Datasets

• DBPedia Structured Information from Wikipedia
  • The DBpedia community uses a flexible and extensible framework to extract different kinds of structured information from Wikipedia.
• The Big Bad NLP Database with 600 Datasets

Machine Learning

• Principle Component Analysis Explaination
  • Explains the working of PCA in a way that you can explain to your grandma! Give some visual understanding of the concept also.
• Generative vs Discriminative Models
• Understanding Latent Space in Machine Learning
  • Aricle explaining what the latent space is, and why it is used.
• Approximate Nearest Neighbors (ANN) Algorithms
  • This blog post comprehensively discusses some of the important ANN Algorithms and their pros and cons.

Deep Learning

• Gradient descent optimzation algorithms
  • It discusses the various algorithms for gradient descent optimization.
• Understanding Emotions -- from Keras to pyTorch
  • A very indepth blog post for related to implementation of LSTMs and Attention models.

Natural Language Processing

• Word Embeddings from Scratch
  • Implimentation of word embeddings from scratch using python and numpy for better understanding.
• Word Embeddings and Glove Embeddings
  • An Intuitive and mathematical understanding of glove embeddings.
• NLP colab notebooks collection repo
  • This repo contains important nlp projects notebooks for practice and learning purposes. The projects are fully documented as the code progresses.
• Document Embedding Techniques
  • This article discusses in detail many different word embedding techniques from simpler to more advanced.
• Text classification using Gensim
  • This article discusses multi-class text classification using doc2vec model, it creates embeddings using doc2vec and then uses Logistic Regression for classification.
• Transformer models Tutorial
  • This blog post gives a breif introduction on how to use hugginface transformers to perform classification and these simple ideas can be than extended later on to perform even more complex tasks.
• HuggingFace officel Notebooks
  • HuggingFace gives the notebooks for their transformer models which are helpful understanding on how to work with these transformers.
• Common Information Retrieval Evaluation Metrics
  • This blog post covers a indepth of some commonly used IR performance evaluation metrics.

Data Science.

• A check list for Data Science Project
  • This blog post covers an extensive and in depth process of a data science project. Infact the website itself is full of data science content.
• Publishing your own PyPi Package
  • This blog post discusses how to upload a python package on PyPi.
• Federated Learning using Pytorch & PySyft
  • A gentle introduction to federated learning using pytorch.

Cloud Computing.