Forwarded from Machine Learning with Python
๐ 500+ Cheat Sheets on Machine Learning
This repository contains materials on virtually everything: from basic algorithms and mathematics to neural networks, transformers, and advanced Deep Learning.
Each cheat sheet includes theory, formulas, Python examples, and tips on how to apply them.
โ You can find it here
https://github.com/gurovic/MLCheatSheets
#MachineLearning #DeepLearning #Python #DataScience #NeuralNetworks #AI
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This repository contains materials on virtually everything: from basic algorithms and mathematics to neural networks, transformers, and advanced Deep Learning.
Each cheat sheet includes theory, formulas, Python examples, and tips on how to apply them.
โ You can find it here
https://github.com/gurovic/MLCheatSheets
#MachineLearning #DeepLearning #Python #DataScience #NeuralNetworks #AI
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Write NumPy code in the familiar way, and then run it on GPUs and TPUs. Automatic differentiation and data batching will be handled automatically, without requiring any extra effort from you.
- Automatically compute derivatives of standard Python and NumPy functions.
- Compile pure functions using
- Combine
https://github.com/jax-ml/jax
https://t.me/MachineLearning9
- Automatically compute derivatives of standard Python and NumPy functions.
- Compile pure functions using
jit to run them on hardware accelerators.- Combine
grad, jit, and vmap in any order and scale your computations across multiple devices.https://github.com/jax-ml/jax
https://t.me/MachineLearning9
GitHub
GitHub - jax-ml/jax: Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more - jax-ml/jax
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"Advanced Calculus" is a free textbook, over 500 pages long, from the mathematics department of Harvard University, dedicated to an in-depth study of mathematical analysis.
It covers topics such as normed vector spaces, compactness and completeness, multi-dimensional mathematical analysis, integration, differential equations, multilinear algebra, differentiable manifolds, exterior calculus, potential theory, classical mechanics, and much more.
This is one of the most interesting resources I've shared in recent years. The book is definitely worth adding to your personal library of high-quality, freely available mathematics textbooks.
https://people.math.harvard.edu/~shlomo/docs/Advanced_Calculus.pdf
https://t.me/MachineLearning9
It covers topics such as normed vector spaces, compactness and completeness, multi-dimensional mathematical analysis, integration, differential equations, multilinear algebra, differentiable manifolds, exterior calculus, potential theory, classical mechanics, and much more.
This is one of the most interesting resources I've shared in recent years. The book is definitely worth adding to your personal library of high-quality, freely available mathematics textbooks.
https://people.math.harvard.edu/~shlomo/docs/Advanced_Calculus.pdf
https://t.me/MachineLearning9
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pandas-guide.pdf
188.3 KB
pandas โ The Mental Model. A practical, example-driven guide in 18 chapters: how tables and indexes actually work, selection and filtering, groupby, reshaping, joins, time series, text, chaining, performance, and what changed in pandas 3.0. Every example is executed, with its real output shown, and the book closes with a one-page cheat sheet.
If the field is short, cut it to: A 31-page practical guide to pandas โ 18 chapters, every example executed with real output, from indexes and selection to groupby, performance and pandas 3.0.
https://t.me/MachineLearning9
If the field is short, cut it to: A 31-page practical guide to pandas โ 18 chapters, every example executed with real output, from indexes and selection to groupby, performance and pandas 3.0.
https://t.me/MachineLearning9
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NumPy looks like a calculator and behaves like a memory model. ๐งฎ
Most tutorials start with functions. This one starts with the five decisions NumPy made before you opened it: an array is one block of memory, shape is a contract between arrays, a slice is a view you can write through, the loop belongs in C, and the axis tells you which direction collapses.
15 chapters, 25 runnable examples, and every output copied from a real execution on NumPy 2.0.2. ๐
Arrays, shapes and the machinery under the math:
https://enghusseinsheikho.substack.com/p/numpy-the-mental-model?r=3hwnr8&utmcampaign=post&utmmedium=web&showWelcomeOnShare=true
Most tutorials start with functions. This one starts with the five decisions NumPy made before you opened it: an array is one block of memory, shape is a contract between arrays, a slice is a view you can write through, the loop belongs in C, and the axis tells you which direction collapses.
15 chapters, 25 runnable examples, and every output copied from a real execution on NumPy 2.0.2. ๐
Arrays, shapes and the machinery under the math:
https://enghusseinsheikho.substack.com/p/numpy-the-mental-model?r=3hwnr8&utmcampaign=post&utmmedium=web&showWelcomeOnShare=true
Substack
NumPy โ The Mental Model
Arrays, shapes and the machinery under the math.
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"Pattern Recognition and Machine Learning" is a free course consisting of 18 lectures, covering the mathematical and computational foundations of machine learning.
The program covers Bayesian decision theory, generative and discriminative classifiers, statistical learning theory, linear models, the support vector machine method, clustering, dimensionality reduction, learning with partially labeled data, and ensemble methods.
It also explores neural networks, backpropagation, convolutional neural networks, deep learning optimization, attention mechanisms, sequence models, and generative models.
All 18 lectures are available for free.
https://chizhizhang.github.io/lecture/2026PRML/
https://t.me/MachineLearning9
The program covers Bayesian decision theory, generative and discriminative classifiers, statistical learning theory, linear models, the support vector machine method, clustering, dimensionality reduction, learning with partially labeled data, and ensemble methods.
It also explores neural networks, backpropagation, convolutional neural networks, deep learning optimization, attention mechanisms, sequence models, and generative models.
All 18 lectures are available for free.
https://chizhizhang.github.io/lecture/2026PRML/
https://t.me/MachineLearning9
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๐ฅ Free IT & Dev Study Kits โ 2026 Prep & Double 11 Giveaway
Prepping for cloud, dev, or AI roles this year? These free kits cover the gaps most people skip โ cert basics, hands-on labs, and mock exams.
๐ Cert E-Book โ Cisco & AWS fundamentals, ideal for first-time cert takers
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๐ช Online Free Course โ a structured path that works great as a refresher before exams
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โ๏ธ Free AI Materials โ from LLM basics to fine-tuning practice notebooks
โ https://tinyurl.com/2z46z8f4
๐ Cloud Study Guide โ cheat-sheet style, handy for interview prep
โ https://tinyurl.com/ymncyj5k
๐ง Free Mock Exam โ test yourself before booking the real thing
โ https://tinyurl.com/y7ffwecz
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Prepping for cloud, dev, or AI roles this year? These free kits cover the gaps most people skip โ cert basics, hands-on labs, and mock exams.
๐ Cert E-Book โ Cisco & AWS fundamentals, ideal for first-time cert takers
โ https://tinyurl.com/ynutkhec
๐ช Online Free Course โ a structured path that works great as a refresher before exams
โ https://tinyurl.com/4kvuk4m6
โ๏ธ Free AI Materials โ from LLM basics to fine-tuning practice notebooks
โ https://tinyurl.com/2z46z8f4
๐ Cloud Study Guide โ cheat-sheet style, handy for interview prep
โ https://tinyurl.com/ymncyj5k
๐ง Free Mock Exam โ test yourself before booking the real thing
โ https://tinyurl.com/y7ffwecz
๐ Double 11 Giveaway โ win iPhone Duo & Amazon Gift Cards
๐ Save this or forward it to your dev/study group โ good resources are worth sharing. ๐ช
๐ Join Community to Draw: https://chat.whatsapp.com/KkbnOUuS55aJdZMtTGrVYL
๐ฒ Personalized help: https://wa.link/xd2
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Columbia University has published a detailed educational resource on the Transformer architecture ๐ .
It covers the mathematical foundations of one of the key technologies in modern AI systems, from recurrent neural networks and the encoder-decoder architecture to attention mechanisms and self-attention.
Particular attention is paid to query, key, and value, scaled dot-product attention, multi-head attention, and softmax.
The main feature of the material is its mathematical approach. The authors use formulas, step-by-step derivations, diagrams, and examples to explain how the individual components of the Transformer are structured and how they interact with each other.
https://www.columbia.edu/~jsl2239/transformers.html
https://t.me/MachineLearning9
It covers the mathematical foundations of one of the key technologies in modern AI systems, from recurrent neural networks and the encoder-decoder architecture to attention mechanisms and self-attention.
Particular attention is paid to query, key, and value, scaled dot-product attention, multi-head attention, and softmax.
The main feature of the material is its mathematical approach. The authors use formulas, step-by-step derivations, diagrams, and examples to explain how the individual components of the Transformer are structured and how they interact with each other.
https://www.columbia.edu/~jsl2239/transformers.html
https://t.me/MachineLearning9
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