DataMListic
Welcome to DataMListic (former WhyML)! On this channel I explain various ML concepts that I encounter in my learning journey. Enjoy the ride! ;)
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Meta's Perception Encoder - Paper Walkthrough
Meta's SAM2: Segment Anything in Images and Videos - Paper Walkthrough
MMaDA: Multimodal Large Diffusion Language Models - Paper Walkthrough
Anthropic Claude 4 - System Card Walkthrough
Google's AlphaEvolve - Paper Walkthrough
Hidden Markov Models (HMM) Part 2 - The Viterbi Algorithm
Hidden Markov Models (HMM) Part 1 - Introduction
An Introduction to Graph Neural Networks
Gaussian Processes
Bayesian Optimization
The RBF Kernel
The Kernel Trick
The Curse of Dimensionality
Cross-Entropy - Explained
Weights Initialization in Neural Networks
Dropout Regularization - Explained
Recommender Systems - Part 3: Issues & Solutions
Recommender Systems - Part 2: Collaborative Filtering
Recommender Systems - Part 1: Content-Based Recommendations
Why L1 Regularization Produces Sparse Weights
Overfitting vs Underfitting - Explained
Confidence Intervals Explained
Z-Test Explained
L1 vs L2 Regularization
Poisson Distribution - Explained
Basic Probability Distributions Explained: Bernoulli, Binomial, Categorical, Multinomial
T-Test Explained
AI Weekly Brief - Week 2: Llama 3.2, OpenAI Voice Mode, Mira Murati leaves OpenAI
AI Weekly Brief - Week 2: LlamaCoder, Eureka, YouTube GenAI, Pixtral 12B
AI Weekly Brief - Week 1: OpenAI o1-preview, DataGemma, AlphaProteo