Deep-ML

New feature on Deep-ML: Paste a job description → get an overview of what to study. We break down the role and surface the ML concepts + questions you’re most likely to be tested on.

We’re starting something new called Deep-Walk. The idea is simple: read an interesting machine learning paper, then meet in Central Park and go for a walk while discussing it. Our first paper will be: Helios: Real Real-Time Long Video Generation Model https://lnkd.in/e_dfHUQr If the paper feels difficult or you’re new to machine learning, don’t let that stop you. The goal isn’t to understand everything perfectly. It’s to walk, talk, ask questions, and learn together. https://lnkd.in/eSmv8ngV 📍 Central Park — Sheep Meadow 🗓 Sunday, March 22 ⏰ 10:00 AM If you enjoy thinking about ML and want to meet others who do too, come join the walk.

We just added a new premium feature on Deep-ML to help people prepare for machine learning interviews. You can now see what topics are commonly discussed during ML interviews with two new features: - Interview Frequency: See how often a topic appears in ML interviews. - Job Filter: Filter questions by company and role. The idea is simple: focus on the topics that actually come up so you can better prepare for your next interview. https://deep-ml.com

Nice job! This is the only way to show off you read and truely understood the paper

Our new Linear Algebra for Machine Learning path just dropped: https://lnkd.in/eBce3Mph This is not meant to be easy. It’s designed to make sure you actually understand the linear algebra that matters for machine learning not just using import ... We focus on the fundamentals, how things work under the hood, and finish with practical problems that force you to apply what you’ve learned in real ML contexts. If you’re looking for something that just makes you feel smart, this isn’t it. This is for people who genuinely want to learn.

Just wrapped up a today's exercise on building a K-Means Clustering function from the ground up on Deep-ML. #machinelearning #python #AIengineering

New on Deep-ML: Premium MLE Interview Prep Collection We just launched a new premium collection on Deep-ML designed specifically to help candidates prepare for Machine Learning Engineer interviews. This collection focuses on: • The core machine learning concepts interviewers actually test • NumPy-first implementations to ensure real understanding • Clear problem breakdowns and edge case reasoning • Practical, interview-style questions that reflect real-world ML work If you are preparing for MLE roles and want structured, hands-on practice beyond standard LeetCode-style questions, this collection was built for you. Learn more here: https://lnkd.in/dWMNBpXQ Feedback is welcome, and best of luck to everyone currently interviewing!

Flipping dimensions today. Today’s focus on Deep-ML was Matrix Transposition. It’s a simple concept of swapping rows and columns and handles everything from data preprocessing to backpropagation algorithms. ↘️ Input: A 2x3 matrix. ↩️ Output: A 3x2 matrix. I implemented this using a list comprehension to keep it clean without importing NumPy. It's a great exercise to keep the logic sharp. Solution snippet attached! 📸 #100DaysOfCode #Python #SoftwareEngineering #LinearAlgebra

We often get used to import numpy or import torch doing the heavy lifting for us, but sometimes it pays to write the logic from scratch. I just tackled this Matrix-Vector Dot Product challenge on Deep-ML. It’s a simple exercise, but it’s essentially the mathematical foundation of how neural networks process information which is multiplying weights by inputs to get an output. If you can't build it with vanilla Python, do you really know how it works? How would you solve this? Nested loops or a one-line list comprehension? #AI #MachineLearning #Python #LinearAlgebra #Engineering #DeepLearning