Machine Learning with Python

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📌 PyTorch Sentiment Analysis - analysis of the emotional component of the text This repository contains different implementations of text analysis in PyTorch: — using a “bag of words” — using a recurrent neural network (RNN) — via convolutional neural network (CNN) - with the help of fashionable transformers 🖥 GitHub

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🟢 Yaoliang Yu, a professor at the School of Computer Science at the University of Waterloo, Canada, has published several free data science courses. These courses include machine learning, data science optimization, linear algebra and deep learning. ✅ The resources of each course include textbooks, assignments, articles and projects during the course. 🔖 Guide to free data science courses at the University of Waterloo: ┌ ➡️ CS794 Fall 2022 └ 🖥 Optimization for Data Science ┌ ➡️ CS480 Fall 2022 └ 🖥 Introduction to Machine Learning ┌ ➡️ CS794 Fall 2021 └ 🖥 Game Theoretic Methods in ML ┌ ➡️ CS480 Fall 2019 └ 🧠 Theory of Deep Learning ┌ ➡️ CS475 Spring 2018 └ 🖥 Computational Linear Algebra 〰️〰️〰️〰️〰️〰️〰️〰️〰️〰️〰️ 😠 More likes 💦 ➡️ more posts ✈️ http://t.me/codeprogrammer ✅

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In 1989, Yann LeCun and his team trained a LeNet 1 CNN, which was able to detect handwriting. They published a video showing how this model can read the numbers that were written manually on a piece of paper, and then the model gives the numbers electronically. The Convolution Neural Network CNN algorithm is considered one of the algorithms that has influenced the world and we find it nowadays in many fields. In general, everything that can be predicted from an image or video is a CNN. Many researchers relied on this algorithm and derived many of the most famous models from it (ResNet, DenseNet, MobileNet, SqueezeNet, VGG) There are many models that come under the name CNN 〰️〰️〰️〰️〰️〰️〰️〰️〰️〰️〰️ 😠 More likes 💦 ➡️ more posts ✈️ http://t.me/codeprogrammer ✅

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🔺 The best GitHub repositories for learning Python ✅ Learn Python for Data Science in 2024 👨🏻‍💻 In the latest data science report 2024 , Python is still the top programming language for data science with 56.7% . Here I have put a list of the best Python repositories for data science , which will improve your coding skills and guide you on the path to data science mastery.💯 1️⃣ Learn Python 3 repo 🖥 A collection of Jupyter notebooks for learning Python. 🐱 GitHub repo link 2️⃣ The Algorithms repo 🖥 All algorithms implemented in Python for training. 🐱 GitHub repo link 3️⃣ Awesome Python repo 🖥 A list of great Python frameworks, libraries, software, and resources. 🐱 GitHub repo link 4️⃣ 100 Days of ML repo 🖥 Learning algorithms and building neural networks without any programming experience. 🐱 GitHub repo link 5️⃣ Cosmic Python book repo 🖥 A book on Python's functional architectural patterns for managing complexity. 🐱 GitHub repo link 6️⃣ A Byte of Python book repo 🖥 If you do not learn Python programming, start with this book. 🐱 GitHub repo link 7️⃣ Python Machine Learning book repo 🖥 Python Machine Learning book code repository. 🐱 GitHub repo link 8️⃣ Repo of interactive interview challenges 🖥 120+ interactive Python coding interview challenges. 🐱 GitHub repo link 9️⃣ Repo of coding problems 🖥 Solutions for various coding/algorithmic problems. 🐱 GitHub repo link 1️⃣ Python Basics repo 🖥 A list of 300 Python interview questions + answer sheet. 🐱 GitHub repo link 1️⃣ Python programming exercises repo 🖥 100+ challenging Python programming exercises. 🐱 GitHub repo link 〰️〰️〰️〰️〰️〰️〰️〰️〰️〰️〰️〰️

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⚡️ Graph Machine Learning Free advanced course: Machine learning on graphs . The course is regularly supplemented with practical problems and slides. The author Xavier Bresson is a professor at the National University of Singapore. ▪ Introduction ▪ Dive into graphs - Lab1: Generate LFR social networks https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code01.ipynb - Lab2: Visualize spectrum of point cloud & grid https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code02.ipynb - Lab3/4: Graph construction for two-moon & text documents https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code03.ipynb https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code04.ipynb ▪ Graph clustering - Lab1: k-means https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code01.ipynb https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code02.ipynb - Lab2: Metis https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code03.ipynb - Lab3/4: NCut/PCut https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code04.ipynb https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code05.ipynb - Lab5: Louvain https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code06.ipynb https://pic.twitter.com/vSXCx364pe ▪ Lectures 4 Graph SVM - Lab1 : Standard/Linear SVM https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code01.ipynb - Lab2 : Soft-Margin SVM https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code02.ipynb - Lab3 : Kernel/Non-Linear SVM https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code03.ipynb - Lab4 : Graph SVM https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code04.ipynb Running instructions: https://storage.googleapis.com/xavierbresson/lectures/CS6208/running_notebooks.pdf 💡 Github ✅ https://t.me/DataScienceT

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