Recommendation Systems This is a workshop on using Machine Learning and Deep Learning Techniques to build Recommendation Systesm Theory: ML & DL Formulation, Prediction vs. Ranking, Similiarity, Biased vs. Unbiased Paradigms: Content-based, Collaborative filtering, Knowledge-based, Hybrid and Ensembles Data: Tabular, Images, Text (Sequences) Models: (Deep) Matrix Factorisation, Auto-Encoders, Wide & Deep, Rank-Learning, Sequence Modelling Methods: Explicit vs. implicit feedback, User-Item matrix, Embeddings, Convolution, Recurrent, Domain Signals: location, time, context, social, Process: Setup, Encode & Embed, Design, Train & Select, Serve & Scale, Measure, Test & Improve Tools: python-data-stack: numpy, pandas, scikit-learn, keras, spacy, implicit, lightfm Notes & Slides Basics: Deep Learning AI Conference 2019: WhiteBoard Notes | In-Class Notebooks Notebooks Movies - Movielens 01-Acquire 02-Augment 03-Refine 04-Transform 05-Evaluation 06-Model-Baseline 07-Feature-extractor 08-Model-Matrix-Factorization 09-Model-Matrix-Factorization-with-Bias 10-Model-MF-NNMF 11-Model-Deep-Matrix-Factorization 12-Model-Neural-Collaborative-Filtering 13-Model-Implicit-Matrix-Factorization 14-Features-Image 15-Features-NLP Ecommerce - YooChoose 01-Data-Preparation 02-Models News - Hackernews Product - Groceries Python Libraries Deep Recommender Libraries Tensorrec - Built on Tensorflow Spotlight - Built on PyTorch TFranking - Built on TensorFlow (Learning to Rank) Matrix Factorisation Based Libraries Implicit - Implicit Matrix Factorisation QMF - Implicit Matrix Factorisation Lightfm - For Hybrid Recommedations Surprise - Scikit-learn type api for traditional alogrithms Similarity Search Libraries Annoy - Approximate Nearest Neighbour NMSLib - kNN methods FAISS - Similarity search and clustering Learning Resources Reference Slides Deep Learning in RecSys by Balázs Hidasi Lessons from Industry RecSys by Xavier Amatriain Architecting Recommendation Systems by James Kirk Recommendation Systems Overview by Raimon and Basilico Benchmarks MovieLens Benchmarks for Traditional Setup Microsoft Tutorial on Recommendation System at KDD 2019 Algorithms & Approaches Collaborative Filtering for Implicit Feedback Datasets Bayesian Personalised Ranking for Implicit Data Logistic Matrix Factorisation Neural Network Matrix Factorisation Neural Collaborative Filtering Variational Autoencoders for Collaborative Filtering Evaluations Evaluating Recommendation Systems

NumberRecognition is a project aimed at recognizing handwritten digits from the MNIST dataset using PyTorch. It includes scripts for training and inference, along with utilities for dataset preparation. This project is ideal for learning the basics of neural networks and digit recognition. This is a "Hello World" program in the field of AI.

Set of notebooks to practice Pytorch from basics to RNN

Pytorch基础操作视频教程系列

Comprehensive PyTorch tutorial for learning and interview preparation - covers basics to advanced topics with hands-on examples

This Repository contains Python Notebooks to learn PyTorch starting from the very basics of implementing gradient descent algorithm in Python and Numpy and slowly moving up the ladder to Deep Learning using PyTorch

This tutorial is designed for beginners of deep learning, introducing the basics of PyTorch to advanced applications step by step.

A repo about deep learning and PyTorch covering basics, projects and ideas

oneNeuron Pytroch basics course docs plus code

From Python basics to Machine Learning and PyTorch Deep Learning - one day at a time, explore it all

Implementation of Neural Network Basics With PyTorch and TensorFlow_ImageAnalysis

This repository contains codes and tutorials for quantum machine learning using PyTorch and Qiskit. It covers topics such as qiskit basics, deep learning fundamentals, and hybrid quantum-classical models. It also demonstrates how to use TorchQuantum, a PyTorch-based framework for quantum neural networks.

This course will guide you from the basics to advanced learning in PyTorch.

CS336-Spring 2025 Assignment 1: Basics （Pytorch & Transformer）

This repository covers Python basics along with core Python 3 development patterns, emphasizing clean code, OOP principles. Introduction to PyTorch — tensors, neural network design, and model training workflows

No description available

No description available

Professional PyTorch CLI learning tool with 24 comprehensive lessons - From tensor basics to ExecutorTorch deployment with interactive examples

Practice using PyTorch include data preprocessing, linear algebra, optimization, neural networks, CNNs, and more to cover ML and DL basics

Basic Pytorch Excercises

This is an introductory python tensorflow2.0+ and pytorch source code tutorial modified based on a tensorflow MOOC tutorial from Peking University, including basic data types and functions as well as the establishment, training and application of artificial neural networks. We provide detailed examples of the basic functions and APIs, and we introduce MLP, CNN and RNN by establishing and training the models on some famous datasets, such as the iris dataset, the mnist dataset, the cifar-10 dataset, the fashion dataset and so on. Similarly, I provide an introductory pytorch source code tutorial developed independently.

PyTorch basics - Linear Regression from scratch

A comprehensive resource to master PyTorch, from basics to advanced techniques, for deep learning enthusiasts and practitioners

implementation of Just Image Transformer from the paper "Back to Basics: Let Denoising Generative Models Denoise" in pytorch

This repository is for study project I created to learn the basics of reinforcement learning neural network and genetic algorithms. This simulation was implemented using Python libraries: Pyglet, Pymunk, Pytorch.

Learning Pytorch basics

A new PyTorch tutorial designed to dig into the basics

The code I wrote while doing the Facebook Pytorch Challenge course offered by Udacity.

Pytorch를 이용한 기초 딥러닝 코딩의 실습 코드입니다

No description available