"Introduction to Machine Learning" is another free textbook on machine learning, approximately 600 pages long, which emphasizes a deep mathematical understanding of the subject. 📚🧮
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. 🤖📈
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). 🎲🧠
The final chapters discuss clustering, principal component analysis (PCA), learning on manifolds, and theoretical estimates of a model's ability to generalize. 🔍📊
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." 💡✨
https://arxiv.org/pdf/2409.02668
#MachineLearning #DeepLearning #AI #Mathematics #DataScience #NeuralNetworks
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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. 🤖📈
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). 🎲🧠
The final chapters discuss clustering, principal component analysis (PCA), learning on manifolds, and theoretical estimates of a model's ability to generalize. 🔍📊
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." 💡✨
https://arxiv.org/pdf/2409.02668
#MachineLearning #DeepLearning #AI #Mathematics #DataScience #NeuralNetworks
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🔖 Google DeepMind has released a book titled "How to Scale Your Model."
It explains how to scale and deploy models even with limited computing resources.
It's useful for those who work on optimizing and deploying ML models.
⛓ Link to the book
https://jax-ml.github.io/scaling-book
It explains how to scale and deploy models even with limited computing resources.
It's useful for those who work on optimizing and deploying ML models.
⛓ Link to the book
https://jax-ml.github.io/scaling-book
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Forwarded from Machine Learning with Python
🔖 A comprehensive collection of resources on MLOps
We've found a useful collection for those who want to go beyond just training models and delve into their deployment and operation.
This repository contains the best materials on deploying, monitoring, automating ML pipelines, CI/CD, and other practices that are essential for production systems.
⛓ Link to GitHub
https://github.com/visenger/awesome-mlops
We've found a useful collection for those who want to go beyond just training models and delve into their deployment and operation.
This repository contains the best materials on deploying, monitoring, automating ML pipelines, CI/CD, and other practices that are essential for production systems.
⛓ Link to GitHub
https://github.com/visenger/awesome-mlops
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Forwarded from Machine Learning with Python
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