Reinforcement Learning Methods and Tutorials ๐ง ๐
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 ๐
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. ๐โจ
#ReinforcementLearning #MachineLearning #AI #DeepLearning #TechTutorials #DataScience
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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 ๐
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. ๐โจ
#ReinforcementLearning #MachineLearning #AI #DeepLearning #TechTutorials #DataScience
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โค12
Top YouTube Channels to Master Tech Skills ๐
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1. SQL ๐ป
๐ youtube.com/@joeyblue1
2. Excel ๐
๐ youtube.com/@excelisfun
3. Statistics ๐
๐ youtube.com/@statquest
4. Math ๐งฎ
๐ youtube.com/results?searchโฆ
5. Python ๐
๐ youtube.com/@BroCodez
6. Data Analysis ๐
๐ youtube.com/@AlexTheAnalyst
7. Machine Learning ๐ค
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8. Deep Learning ๐ง
๐ youtube.com/@deeplizard
9. Java โ
๐ youtube.com/@Telusko
10. Big Data ๐ฆ
๐ youtube.com/@thedatatech
11. Data Engineering โ๏ธ
๐ youtube.com/@dataengineeriโฆ
12. NLP (Natural Language Processing) ๐ฃ๏ธ
๐ youtube.com/@codebasics
13. Computer Vision & AI ๐๏ธ
๐ youtube.com/@murtazasworksโฆ
14. Generative AI โจ
๐ youtube.com/@sunnysavita10
15. University-Level Courses ๐
๐ youtube.com/@stanfordonline
๐ youtube.com/@mitocw
16. All-in-One Learning ๐
๐ youtube.com/@freecodecamp
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โค14๐ฅ1
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๐ A useful training tool for Data Scientists ๐
๐ซก Real-world tasks from IT companies;
๐ซก SQL practice;
๐ซก Python tasks;
๐ซก Preparation for Data Science interviews.
โ Link to the training tool
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๐ซก Real-world tasks from IT companies;
๐ซก SQL practice;
๐ซก Python tasks;
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โ Link to the training tool
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Forwarded from Machine Learning
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A Powerful Alternative to Pandas ๐
This is an optimized replacement for Pandas that can significantly speed up data processing without requiring major changes to your code. โ๏ธ
To get started, simply replace a single import:
Performance Benchmarks demonstrate speed improvements in various use cases. ๐
More: https://colab.research.google.com/drive/1UIokuJ4cytoiVSabRDqcziDXOan8bVua?usp=sharing
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This is an optimized replacement for Pandas that can significantly speed up data processing without requiring major changes to your code. โ๏ธ
To get started, simply replace a single import:
import fireducks.pandas as pd
Performance Benchmarks demonstrate speed improvements in various use cases. ๐
More: https://colab.research.google.com/drive/1UIokuJ4cytoiVSabRDqcziDXOan8bVua?usp=sharing
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A collection of resources on MLOps for those who want to understand how machine learning systems are brought to production. ๐๐ค
https://github.com/visenger/awesome-mlops
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https://github.com/visenger/awesome-mlops
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Forwarded from Machine Learning
๐ Over 300 real-world case studies of ML systems from top companies. ๐ค
We found a repository that collects genuine ML engineering experience โ not theory from textbooks, but real stories of implementing models in production. ๐
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. ๐๏ธ
โ Link to GitHub
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We found a repository that collects genuine ML engineering experience โ not theory from textbooks, but real stories of implementing models in production. ๐
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. ๐๏ธ
โ Link to GitHub
https://github.com/Engineer1999/A-Curated-List-of-ML-System-Design-Case-Studies
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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. ๐๐งฎ
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. ๐ค๐
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). ๐ฒ๐ง
The final chapters discuss clustering, principal component analysis (PCA), learning on manifolds, and theoretical estimates of a model's ability to generalize. ๐๐
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." ๐กโจ
https://arxiv.org/pdf/2409.02668
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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. ๐ค๐
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). ๐ฒ๐ง
The final chapters discuss clustering, principal component analysis (PCA), learning on manifolds, and theoretical estimates of a model's ability to generalize. ๐๐
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." ๐กโจ
https://arxiv.org/pdf/2409.02668
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DS FULL ARCHIVE.pdf
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DS FULL ARCHIVE ๐
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