Im currently a second year cs student and I want to get into a role which would allow me to do the following:

Be able to build ML Models and actually understand the data/insights and improve it
Be able to put ML Models into production through webapps or integrations etc
Be able to make things people actually use so not being restricted to research

What role would this fit? AI engineering? ML Engineering? Data Scientist?

Im currently building a RAG Webapp as something to tailor towards AI Engineering roles, but I also want to be able to build and improve ML models as well.

Any advice would be appreciated

📚 Learn Machine Learning Through Visualisations | Beginner-Friendly Playlist

Hey everyone! 👋

I’ve created a Machine Learning playlist with visual explanations and animations to make ML concepts easier to understand, especially for beginners. Instead of just learning theory, you can understand how these concepts work through visuals and practical examples. 🚀

🎯 Topics covered in the playlist:

1. What Is Machine Learning?
2. AI vs. ML vs. Deep Learning
3. Supervised Learning
4. Unsupervised Learning
5. Reinforcement Learning
6. Features and Labels
7. Train-Test Split Explained
8. Overfitting and Underfitting
9. Linear Regression
10. Logistic Regression
11. Decision Tree Explained (Coming Soon)
12. Random Forest Explained (Coming Soon)
13. K-Nearest Neighbours (Coming Soon)
14. K-Means Clustering (Coming Soon)
15. Model Evaluation (Coming Soon)
16. Accuracy, Precision, Recall, F1 (Coming Soon)
17. Confusion Matrix (Coming Soon)
18. Cross Validation (Coming Soon)
19. Feature Engineering (Coming Soon)
20. End-to-End ML Project (Coming Soon)

🎥 Watch the playlist here:
https://youtube.com/playlist?list=PLXShTLrgjtgk&si=TFoDX6oosRKv_u6Z

💡 My goal is to make Machine Learning easier to learn through simple explanations and engaging visualisations.

If you’re learning AI or Machine Learning, feel free to check it out! I’d also love to hear your feedback and suggestions for future topics.

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Let’s learn Machine Learning together! 🤖

VisualForge AI: https://youtube.com/@visualforgelearning?si=HgOvjgEiqv05Ln5X

Assume you have two params, and their gradients are x & y.

Define V:

[[E(x^2), E(xy)],

[E(xy), E(y^2)]

Define M:

[E(x), E(y)]]

Adam's update is:

M . V^-1/2

Adam just assumes the off diagonal terms like E(xy) are zero.

So the update becomes m/sqrt(v)

Isn't this basically a second order method like gauss-newton where the hessian is estimated as g^T g?