datamlistic

Explaining machine learning in simple terms on my YouTube channel.

[datamlistic @datamlistic]

why you wouldn't take this bet

[datamlistic @datamlistic]

Neural networks without activation functions are just linear models.

Watch full video here: https://t.co/D2GgeSo3H1

#AI #MachineLearning #DeepLearning

[datamlistic @datamlistic]

Decision trees are one of the most intuitive machine learning models — but how do they actually work?
This video shows how simple yes/no questions can classify data and create decision boundaries.

Watch here: https://t.co/l1xTWgTnfu

#machinelearning #datascience #ai

[Andrej Karpathy @karpathy]

One common issue with personalization in all LLMs is how distracting memory seems to be for the models. A single question from 2 months ago about some topic can keep coming up as some kind of a deep interest of mine with undue mentions in perpetuity. Some kind of trying too hard.

[datamlistic @datamlistic]

Most people trust a single model. That’s a mistake.
Random Forest shows why many simple models can outperform one complex one — by reducing noise and improving stability.

Clear explanation with visuals 👇
https://t.co/gCRb0Phlrn

#machinelearning #ai #datascience

[datamlistic @datamlistic]

How big is a vector?
Turns out… there’s more than one answer.
L1, L2, and infinity norms explained with simple intuition and visuals — plus why they matter in machine learning.

Watch here: https://t.co/mEFmmk3o0z

#machinelearning #datascience #math

[datamlistic @datamlistic]

Activation functions are the reason neural networks actually work.
ReLU, tanh, sigmoid, softmax — when to use each and why they matter for gradient flow and learning.

Watch here: https://t.co/D2GgeSnvRt

#AI #MachineLearning #DeepLearning

[datamlistic @datamlistic]

Understanding convolutional layers is essential for anyone learning AI and computer vision.
This video explains how CNNs process images using filters, kernels, and feature maps.

Watch here: https://t.co/XAF6uUnnrH

#ai #machinelearning #deeplearning

[datamlistic @datamlistic]

Convolution is one of the core ideas behind modern AI.
It powers CNNs, computer vision, and image recognition.

Watch the full video here: https://t.co/XAF6uUnnrH

#ai #machinelearning #deeplearning #computervision

[datamlistic @datamlistic]

Neural networks don’t output probabilities — they output logits.
So how do models convert those raw scores into probabilities?

Find the answer here: https://t.co/ZnubfVepnw

#ai #machinelearning #deeplearning

[datamlistic @datamlistic]

Maximum Likelihood is one of the most important ideas in statistics and machine learning.

Watc the full video here: https://t.co/kQi9KjFvlO

#machinelearning #datascience #statistics

[datamlistic @datamlistic]

Images aren’t just numbers in a vector. Their spatial structure matters.

This video explains how Convolutional Neural Networks (CNNs) use kernels, feature maps, pooling, and inductive biases to make image recognition possible.

Watch here: https://t.co/XAF6uUnnrH

[datamlistic @datamlistic]

K-Means is one of the most important algorithms in machine learning. This video explains how K-Means clustering groups data, how centroids move during training, and why it’s widely used in data science and AI.

Watch here: https://t.co/8rgq6clkKW

[datamlistic @datamlistic]

Maximum Likelihood Estimation is one of the most important ideas in statistics and machine learning.

🎥 Watch here: https://t.co/kQi9KjEXwg

#machinelearning #datascience #statistics

[datamlistic @datamlistic]

Most real-world data has no labels. K-Means Clustering shows how structure can still emerge — using nothing more than distance, centroids, and iteration.

Watch here: https://t.co/8rgq6clkKW

#machinelearning #datascience #ai #clustering

[datamlistic @datamlistic]

Support vectors are the reason SVM works so well.
Understanding them makes the geometry of machine learning much clearer.

Watch the full video here: https://t.co/UjRsnvBXDu

#machinelearning #datascience #ai #ml

[datamlistic @datamlistic]

Support Vector Machines explained visually and mathematically.

• Classification problem
• Maximum margin intuition
• Why support vectors matter
• The kernel trick

Watch here: https://t.co/UjRsnvBXDu

#machinelearning #ai #datascience #svm

[datamlistic @datamlistic]

Recurrent Neural Networks (RNNs) are the foundation of sequence modeling in AI.

Watch the full explanation here:
🔗 https://t.co/ssBoLBmkDZ

#ai #machinelearning #deeplearning #datascience

[datamlistic @datamlistic]

Recurrent Neural Networks introduced memory into neural networks.

This video explains:
• Hidden states
• The recurrent cell
• The RNN equation
• Why tanh prevents exploding activations
Clear math. Clean intuition. No fluff.

Watch here: https://t.co/ssBoLBmkDZ

[datamlistic @datamlistic]

What is the rank of a matrix — and why does it matter in linear algebra?

Watch the full video here: https://t.co/Syo9xpCnlf

#linearalgebra #mathematics #machinelearning #datascience