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this is how machine learning actually works (gradient descent with bad starting parameters)

38,694 Aufrufe • vor 1 Jahr •via X (Twitter)

10 Kommentare

Profilbild von luffy
luffyvor 1 Jahr

sgd be like

Profilbild von Rainmaker
Rainmakervor 2 Jahren

Can Machine Learning beat the market? Check out this post on my free Substack where I share code and commentary for an XGBoost model and a Random Forest model that both deliver powerful performances.

Profilbild von Kandrej Arpathy
Kandrej Arpathyvor 1 Jahr

that’s an oversimplification that is borderline fake news. you can make much memes that are both educational and funny

Profilbild von Salomon Metre
Salomon Metrevor 1 Jahr

😅😅

Profilbild von roro
rorovor 1 Jahr

GAN training

Profilbild von Nic B
Nic Bvor 1 Jahr

🤣

Profilbild von Jebin Einstein
Jebin Einsteinvor 1 Jahr

Just now learning “kaiming” and my timeline showing me post related to it 🤯🤯🤯🤯

Profilbild von Michael
Michaelvor 1 Jahr

Very accurate😀

Profilbild von Pehdrew
Pehdrewvor 1 Jahr

0:25 don't elaborate! 😎

Profilbild von Yacine Mahdid
Yacine Mahdidvor 1 Jahr

Reinforcement learning in the 40th step

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ℏεsam

117,570 Aufrufe • vor 1 Jahr

if you're struggling on where to start learning ML, here’s a playlist of 30 youtube videos to learn machine learning fundamentals from scratch "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

ℏεsam

108,861 Aufrufe • vor 1 Jahr