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Deep-Path: The entire Deep-ML catalog, organized into one structured learning path featuring nearly 2,000 machine learning coding problems and hands-on projects. Throughout the path, you'll work through the math, implement concepts from scratch using minimal libraries like NumPy or CUDA, and build projects step by step There's also Deep-Path...

64,374 Aufrufe • vor 8 Tagen •via X (Twitter)

20 Kommentare

Profilbild von SamSepiol
SamSepiolvor 7 Tagen

The path is long but it’s really fun 🤩

Profilbild von Deep-ML
Deep-MLvor 7 Tagen

Assuming most people will work on the essentials path but the full one is an option if you really want to understand one part really well

Profilbild von SamSepiol
SamSepiolvor 7 Tagen

Absolutely i love deep-mL especially the projects and interview path , it’s closely what i find in interview questions

Profilbild von Deep-ML
Deep-MLvor 7 Tagen

Glad to hear you like the site. Let me know if you have any interviews coming up, and I could try making an interview prep collection for that company

Profilbild von infy
infyvor 7 Tagen

would be great if you could implement some sort of spaced repetition feature

Profilbild von Deep-ML
Deep-MLvor 7 Tagen

What do you mean by that?

Profilbild von Boardy
Boardyvor 8 Tagen

the treasure is 1984 steps away

Profilbild von Raka
Rakavor 8 Tagen

looks so good

Profilbild von Ian P
Ian Pvor 7 Tagen

Really cool way of showing the curriculum, caught my eye. I'll take a look into it and give it a try!

Profilbild von Deep-ML
Deep-MLvor 7 Tagen

Thanks, let me know how you like it!

Profilbild von Prashant
Prashantvor 8 Tagen

❤️❤️

Profilbild von Daniel
Danielvor 8 Tagen

My goal is to understand AI from the fundamentals, eventually read papers and train models. I work mostly on systems, and infra & devops. Tried in 2022 but couldn't finish (life). Can I jump straight into Deep-ML and fill the gaps as I go, or should I 1/2

Profilbild von michaelvu
michaelvuvor 7 Tagen

Definitely take a look

Profilbild von Adolf.
Adolf.vor 7 Tagen

I'm interested to know if I can move to this after learning python

Profilbild von Deep-ML
Deep-MLvor 7 Tagen

Yes but it will be a learning through fire, you will get stuck and need to use chat or the AI tutor to get unstuck I think learning through solving is the best way to learn but it is difficult at times

Profilbild von Adolf.
Adolf.vor 7 Tagen

Can you explain better, pls

Profilbild von Deep-ML
Deep-MLvor 7 Tagen

If you know Python but nothing about machine learning, you can absolutely start learning by solving problems. You’ll probably get stuck on certain concepts or wonder why something matters. That’s where AI can be really helpful. Use it to understand the bigger picture, explain unfamiliar ideas, or guide you when you’re stuck. I think problem-based learning is one of the best ways to learn because it forces you to actually understand what you’re doing. When you watch a video or read something, it’s easy to feel like you understand a concept. But when you try to implement it yourself, you quickly find out what you actually know and what you don’t.

Profilbild von Adolf.
Adolf.vor 7 Tagen

I will try it, thanks for your response

Profilbild von 🍀🃏
🍀🃏vor 7 Tagen

This is cool

Profilbild von AA
AAvor 7 Tagen

@praveeen_si

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ℏεsam

109,764 Aufrufe • vor 1 Jahr

a playlist of 30 youtube videos to learn machine learning fundamentals from scratch if you're struggling on where to start learning ML, this list goes this "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

117,748 Aufrufe • vor 1 Jahr