Resources complimenting the Machine Learning Course led in the Faculty of mathematics and informatics part of Sofia University.

Related tags

Deep Learningpythondata-sciencemachine-learningdeep-learningtensorflownumpyscikit-learnsklearnpandaspython3matplotlib
Overview

Machine Learning and Data Mining, Summer 2021-2022

How to learn data science and machine learning?

  1. Programming. Learn Python.
  2. Basic Statistics. Take a look at the amazing YouTube channel in the Maths section.
  3. Databases. Learn SQL. Check out mode.com.
  4. Exploratory Data Analysis. Learn how to work with NumPy, Pandas, Matplotlib, Seaborn.
  5. Machine Learning. Learn the various algorithms. Explore Scikit learn.
  6. Data scraping. Explore the Beautiful soup library.
  7. Study the niches: deep learning, nlp (natural language processing), cv (computer vision).
  8. Deployment. Explore Flask, Streamlit, Django.

Framework

  1. Learn just enough.
  2. Do a project. For example choose one from Kaggle.
  3. Iterate.
  4. Accountability. Show off your work on Github.

Most important APIs

Resources

Python

Markdown

Maths

Machine learning and Deep learning

Books

Owner
Simeon Hristov
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Simeon Hristov
GitHub Repository
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

Awesome production machine learning This repository contains a curated list of awesome open source libraries that will help you deploy, monitor, versi

The Institute for Ethical Machine Learning 12.9k Jan 04, 2023
This is an official implementation for the WTW Dataset in "Parsing Table Structures in the Wild " on table detection and table structure recognition.

WTW-Dataset This is an official implementation for the WTW Dataset in "Parsing Table Structures in the Wild " on ICCV 2021. Here, you can download the

109 Dec 29, 2022
CLIPort: What and Where Pathways for Robotic Manipulation

CLIPort CLIPort: What and Where Pathways for Robotic Manipulation Mohit Shridhar, Lucas Manuelli, Dieter Fox CoRL 2021 CLIPort is an end-to-end imitat

246 Dec 11, 2022
prior-based-losses-for-medical-image-segmentation

Repository for papers: Benchmark: Effect of Prior-based Losses on Segmentation Performance: A Benchmark Midl: A Surprisingly Effective Perimeter-based

Rosana EL JURDI 9 Sep 07, 2022
Official implementation for the paper: Multi-label Classification with Partial Annotations using Class-aware Selective Loss

Multi-label Classification with Partial Annotations using Class-aware Selective Loss Paper | Pretrained models Official PyTorch Implementation Emanuel

99 Dec 27, 2022
A flexible ML framework built to simplify medical image reconstruction and analysis experimentation.

meddlr Getting Started Meddlr is a config-driven ML framework built to simplify medical image reconstruction and analysis problems. Installation To av

Arjun Desai 36 Dec 16, 2022
Libraries, tools and tasks created and used at DeepMind Robotics.

dm_robotics: Libraries, tools, and tasks created and used for Robotics research at DeepMind. Package overview Package Summary Transformations Rigid bo

DeepMind 273 Jan 06, 2023
Example Of Fine-Tuning BERT For Named-Entity Recognition Task And Preparing For Cloud Deployment Using Flask, React, And Docker

Example Of Fine-Tuning BERT For Named-Entity Recognition Task And Preparing For Cloud Deployment Using Flask, React, And Docker This repository contai

Nikita 12 Dec 14, 2022
Adabelief-Optimizer - Repository for NeurIPS 2020 Spotlight "AdaBelief Optimizer: Adapting stepsizes by the belief in observed gradients"

AdaBelief Optimizer NeurIPS 2020 Spotlight, trains fast as Adam, generalizes well as SGD, and is stable to train GANs. Release of package We have rele

Juntang Zhuang 998 Dec 29, 2022
PyTorch code accompanying the paper "Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning" (NeurIPS 2021).

HIGL This is a PyTorch implementation for our paper: Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning (NeurIPS 2021). Our cod

Junsu Kim 20 Dec 14, 2022
A Parameter-free Deep Embedded Clustering Method for Single-cell RNA-seq Data

A Parameter-free Deep Embedded Clustering Method for Single-cell RNA-seq Data Overview Clustering analysis is widely utilized in single-cell RNA-seque

AI-Biomed @NSCC-gz 3 May 08, 2022
FAMIE is a comprehensive and efficient active learning (AL) toolkit for multilingual information extraction (IE)

FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction

18 Sep 01, 2022
Attention mechanism with MNIST dataset

[TensorFlow] Attention mechanism with MNIST dataset Usage $ python run.py Result Training Loss graph. Test Each figure shows input digit, attention ma

YeongHyeon Park 12 Jun 10, 2022
Python package for Bayesian Machine Learning with scikit-learn API

Python package for Bayesian Machine Learning with scikit-learn API Installing & Upgrading package pip install https://github.com/AmazaspShumik/sklearn

Amazasp Shaumyan 482 Jan 04, 2023
Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset

Semantic Segmentation on MIT ADE20K dataset in PyTorch This is a PyTorch implementation of semantic segmentation models on MIT ADE20K scene parsing da

MIT CSAIL Computer Vision 4.5k Jan 08, 2023
Credit fraud detection in Python using a Jupyter Notebook

Credit-Fraud-Detection - Credit fraud detection in Python using a Jupyter Notebook , using three classification models (Random Forest, Gaussian Naive Bayes, Logistic Regression) from the sklearn libr

Ali Akram 4 Dec 28, 2021
School of Artificial Intelligence at the Nanjing University (NJU)School of Artificial Intelligence at the Nanjing University (NJU)

F-Principle This is an exercise problem of the digital signal processing (DSP) course at School of Artificial Intelligence at the Nanjing University (

Thyrix 5 Nov 23, 2022
Intent parsing and slot filling in PyTorch with seq2seq + attention

PyTorch Seq2Seq Intent Parsing Reframing intent parsing as a human - machine translation task. Work in progress successor to torch-seq2seq-intent-pars

Sean Robertson 160 Jan 07, 2023
Pre-trained models for a Cascaded-FCN in caffe and tensorflow that segments

Cascaded-FCN This repository contains the pre-trained models for a Cascaded-FCN in caffe and tensorflow that segments the liver and its lesions out of

300 Nov 22, 2022
All of the figures and notebooks for my deep learning book, for free!

"Deep Learning - A Visual Approach" by Andrew Glassner This is the official repo for my book from No Starch Press. Ordering the book My book is called

Andrew Glassner 227 Jan 04, 2023