Explaining machine learning in simple terms on my YouTube channel.
Joined November 2022
- Tweets 468
- Following 40
- Followers 338
- Likes 783
407 Photos and videos
Mar 28
Neural networks without activation functions are just linear models.
Watch full video here: youtu.be/slp222E_0d4
#AI #MachineLearning #DeepLearning
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Mar 28
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: youtu.be/lfCAYVZtiEw
#machinelearning #datascience #ai
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datamlistic retweeted
Mar 25
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.
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Mar 24
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 👇
youtu.be/ru8nGIJEzmU
#machinelearning #ai #datascience
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Mar 22
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: youtu.be/Xw49EnkM4ls
#machinelearning #datascience #math
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Mar 18
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: youtu.be/slp222E_0d4
#AI #MachineLearning #DeepLearning
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Mar 17
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: youtu.be/YGILT182T6w
#ai #machinelearning #deeplearning
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Mar 13
Convolution is one of the core ideas behind modern AI.
It powers CNNs, computer vision, and image recognition.
Watch the full video here: youtu.be/YGILT182T6w
#ai #machinelearning #deeplearning #computervision
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Mar 12
Neural networks don’t output probabilities — they output logits.
So how do models convert those raw scores into probabilities?
Find the answer here: youtu.be/oJU6-qW6xZU
#ai #machinelearning #deeplearning
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Mar 11
Maximum Likelihood is one of the most important ideas in statistics and machine learning.
Watc the full video here: youtu.be/Pk7kDdWuG1Q
#machinelearning #datascience #statistics
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Mar 10
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: youtu.be/YGILT182T6w
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Mar 9
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: youtu.be/dyG9cj5RKL0
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Mar 7
Maximum Likelihood Estimation is one of the most important ideas in statistics and machine learning.
🎥 Watch here: youtu.be/Pk7kDdWuG1Q
#machinelearning #datascience #statistics
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Mar 4
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: youtu.be/dyG9cj5RKL0
#machinelearning #datascience #ai #clustering
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Mar 3
Support vectors are the reason SVM works so well.
Understanding them makes the geometry of machine learning much clearer.
Watch the full video here: youtu.be/K1EcCjDD_q4
#machinelearning #datascience #ai #ml
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Mar 2
Support Vector Machines explained visually and mathematically.
• Classification problem
• Maximum margin intuition
• Why support vectors matter
• The kernel trick
Watch here: youtu.be/K1EcCjDD_q4
#machinelearning #ai #datascience #svm
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Mar 1
Recurrent Neural Networks (RNNs) are the foundation of sequence modeling in AI.
Watch the full explanation here:
🔗 youtu.be/8G1fImBCMcQ
#ai #machinelearning #deeplearning #datascience
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Feb 28
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: youtu.be/8G1fImBCMcQ
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Feb 24
What is the rank of a matrix — and why does it matter in linear algebra?
Watch the full video here: youtu.be/A_7pVoexIaE
#linearalgebra #mathematics #machinelearning #datascience
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