Abrar

Data scientist in progress, Reading, master student, developer

[TensorTonic @TensorTonic]

All the math you need to understand Machine Learning:

Linear Algebra

> Vectors, dot products, norms
> Matrix multiplication and transpose
> Eigenvalues and eigenvectors
> Singular Value Decomposition (SVD)
> Matrix inverses and pseudo-inverses

Every dataset is a matrix. Every model transformation is a matrix operation. When you implement PCA from scratch, you stop fearing linear algebra forever.
Read from here: https://t.co/Lje5pw14ve

Probability and Statistics

> Bayes' theorem
> Distributions: Gaussian, Bernoulli, Multinomial
> Expectation, variance, covariance
> Maximum Likelihood Estimation
> MAP estimation
> Conditional probability

Every ML model is making a probabilistic bet. Naive Bayes, GMMs, VAEs: all just probability applied differently.
Read here: https://t.co/Lje5pw14ve

Calculus

> Derivatives and partial derivatives
> Chain rule (this is literally backpropagation)
> Gradients and Jacobians
> Hessians (second-order intuition, not computation)
> Multivariate function optimization

You need to understand what a gradient means geometrically: which direction makes the loss go down.
Read here: https://t.co/Lje5pw14ve

Optimization

> Gradient descent and its variants (SGD, Adam, RMSProp)
> Learning rates and convergence
> Convexity (and why non-convex still works)
> Loss functions: MSE, cross-entropy, hinge loss
> Regularization: L1, L2, and why they work geometrically

This is where math becomes code.
Read here: https://t.co/Lje5pw14ve

Information Theory

> Entropy
> Cross-entropy (yes, your loss function)
> KL divergence
> Mutual information

Most people skip this. Don't.
Read here: https://t.co/Lje5pw14ve

That's it.

These five areas, practiced through real implementations, cover 95% of the math behind every ML model from linear regression to transformers.

That's what we built TensorTonic for. 200+ problems where you implement the math from scratch, with interactive visualizations that show you what every operation does geometrically.

Link - https://t.co/jni5KcSizC

[Abrar @Pastel_Logic]

اليوم الـ15
درست مدة ساعة
بدأت في الاسبوع الثاني من الدورة

[Abrar @Pastel_Logic]

حبدأ تجربة 90 يوم في تعلم الـ Machine Learning و Data Science
اليوم الأول: انهيت جزيئة من الاسبوع الثالث من دورة: Calculus for Machine Learning and Data Science
من موقع: https://t.co/oZBad0fIVg

[Abrar @Pastel_Logic]

اليوم الرابع عشر
درست مدة ساعة
وانهيت الأسبوع الأول من كورس الإحصاء والإحتمالات

[Abrar @Pastel_Logic]

@Aihmed_ml بالتوفيق .. هل تتعلم على منهج محدد ككتاب او دورة تدريبية؟

[Abrar @Pastel_Logic]

من الحلقات اللي اعجبتني وحبيت اشاركها لبودكاست big think
https://t.co/3V9w9g2An7

[Abrar @Pastel_Logic]

أقرأ الآن:

[Abrar @Pastel_Logic]

الى الآن ما انتهيت من أي كورس علقت في الدرس الاخير لدورة Linear algebra🙄