Machine Learning with Python
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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.

Admin: @HusseinSheikho || @Hussein_Sheikho
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Reinforcement Learning Methods and Tutorials ๐Ÿง ๐Ÿ“š

In these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years.

Learning Resources: https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow ๐Ÿš€

Here's a collection of simple materials on methods and practical guides, covering both basic reinforcement learning algorithms and modern, recently developed, and updated advanced algorithms. ๐Ÿ“–โœจ

#ReinforcementLearning #MachineLearning #AI #DeepLearning #TechTutorials #DataScience

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Top YouTube Channels to Master Tech Skills ๐Ÿš€

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#TechSkills #YouTube #DataScience #Programming #MachineLearning #LearnTech

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๐Ÿ”– A useful training tool for Data Scientists ๐Ÿ“Š

๐Ÿซก Real-world tasks from IT companies;
๐Ÿซก SQL practice;
๐Ÿซก Python tasks;
๐Ÿซก Preparation for Data Science interviews.

โ›“ Link to the training tool
https://www.stratascratch.com/

๐Ÿท #DataScience #SQL #Python #InterviewPrep #TechTraining #DataAnalyst

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Forwarded from Machine Learning
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A Powerful Alternative to Pandas ๐Ÿš€

This is an optimized replacement for Pandas that can significantly speed up data processing without requiring major changes to your code. โš™๏ธ

To get started, simply replace a single import:

import fireducks.pandas as pd

Performance Benchmarks demonstrate speed improvements in various use cases. ๐Ÿ“ˆ

More: https://colab.research.google.com/drive/1UIokuJ4cytoiVSabRDqcziDXOan8bVua?usp=sharing

#Pandas #Python #DataScience #Performance #Fireducks #BigData

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A collection of resources on MLOps for those who want to understand how machine learning systems are brought to production. ๐Ÿš€๐Ÿค–

https://github.com/visenger/awesome-mlops

#MLOps #MachineLearning #DevOps #AI #DataScience #TechResources

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Forwarded from Machine Learning
๐Ÿ”– Over 300 real-world case studies of ML systems from top companies. ๐Ÿค–

We found a repository that collects genuine ML engineering experience โ€“ not theory from textbooks, but real stories of implementing models in production. ๐Ÿ“š

Inside, you'll find case studies from Uber, Netflix, Google, and other companies: how they built the architecture, what problems arose, where the systems failed, and what solutions helped them recover. ๐Ÿ—๏ธ

โ›“ Link to GitHub
https://github.com/Engineer1999/A-Curated-List-of-ML-System-Design-Case-Studies

#MachineLearning #MLCaseStudies #DataScience #Engineering #Uber #Netflix

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Forwarded from Machine Learning
"Introduction to Machine Learning" is another free textbook on machine learning, approximately 600 pages long, which emphasizes a deep mathematical understanding of the subject. ๐Ÿ“š๐Ÿงฎ

The book begins with the mathematical foundations necessary for further study: linear algebra, mathematical analysis, probability theory, matrix analysis, and optimization methods. It then covers the main supervised learning algorithms: linear and logistic regression, the k-nearest neighbors method, decision trees, random forests, boosting, and neural networks. ๐Ÿค–๐Ÿ“ˆ

A significant portion of the book is dedicated to probabilistic and generative models. It discusses Monte Carlo methods, graphical models, Bayesian networks, variational methods, normalizing flows, variational autoencoders (VAEs), and generative adversarial networks (GANs). ๐ŸŽฒ๐Ÿง 

The final chapters discuss clustering, principal component analysis (PCA), learning on manifolds, and theoretical estimates of a model's ability to generalize. ๐Ÿ”๐Ÿ“Š

In my opinion, this is an excellent resource for those who want to gain a broad understanding of machine learning and understand the mathematics underlying the key methods, rather than treating them as "black boxes." ๐Ÿ’กโœจ

https://arxiv.org/pdf/2409.02668

#MachineLearning #DeepLearning #AI #Mathematics #DataScience #NeuralNetworks

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DS FULL ARCHIVE.pdf
37.1 MB
DS FULL ARCHIVE ๐Ÿ—‚

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#DS #Archive #DataScience #Tech #News #Update
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Forwarded from Machine Learning
๐Ÿ“š "Natural Language Processing and Large Language Models" is a new open-access book from Springer, written by Chengqing Zong, Yang Zhao, and Yanjun Ma.

It's almost 400 pages long and provides an introduction to modern natural language processing and large language models.

Inside, you'll find information on: neural networks, distributed representations, language models, Transformers, BERT, GPT, tokenization, sentiment analysis, information extraction, text summarization, natural language understanding, machine translation, question answering, and RLHF.

In my opinion, this is a good reference guide for those who want to understand these topics without a very high barrier to entry. I would recommend it. โœจ

https://link.springer.com/book/10.1007/978-981-92-0682-7

#NLP #LLM #ArtificialIntelligence #MachineLearning #DataScience #TechBooks

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