Welcome to Data Santa, my digital diary where numbers dance, and algorithms whisper the secrets of the universe. I believe in the power of knowledge, the magic of math, and the art of programming.

Implementing Cross-Entropy Loss and Log-Sum-Exp in MicroTorch
17:09
Implementing Cross-Entropy Loss and Log-Sum-Exp in MicroTorch
Building Loss Functions in MicroTorch: Expected Risk, Empirical Risk, and Binary Cross-Entropy
14:08
Building Loss Functions in MicroTorch: Expected Risk, Empirical Risk, and Binary Cross-Entropy
Tensor Operations in MicroTorch: Min, Max, Sum, Mean, and Activation Functions
13:20
Tensor Operations in MicroTorch: Min, Max, Sum, Mean, and Activation Functions
Essential Tensor Operations in MicroTorch: Mastering Max with Backward Mode
11:05
Essential Tensor Operations in MicroTorch: Mastering Max with Backward Mode
Building MicroTorch: Indexing and Absolute Value Operations for Tensors
9:38
Building MicroTorch: Indexing and Absolute Value Operations for Tensors
Mastering Tensor Operations: Comparison, Where, and More in MicroTorch
16:31
Mastering Tensor Operations: Comparison, Where, and More in MicroTorch
Building PyTorch: Enhancing MicroTorch with Squeeze, View, and Clip Operations
16:21
Building PyTorch: Enhancing MicroTorch with Squeeze, View, and Clip Operations
Building PyTorch: Enriching MicroTorch with Logs, Exponents, and Activation Functions
20:07
Building PyTorch: Enriching MicroTorch with Logs, Exponents, and Activation Functions
Building PyTorch: Overloading Operators for Subtraction and Multiplication in MicroTorch
14:46
Building PyTorch: Overloading Operators for Subtraction and Multiplication in MicroTorch
Building PyTorch: Adding Broadcasting and Addition Operations to MicroTorch
20:40
Building PyTorch: Adding Broadcasting and Addition Operations to MicroTorch
Building PyTorch: Mastering Matrix Multiplication and Linear Layers in MicroTorch
17:22
Building PyTorch: Mastering Matrix Multiplication and Linear Layers in MicroTorch
Building PyTorch: Crafting Linear Layers and Parameter Counting in MicroTorch
16:23
Building PyTorch: Crafting Linear Layers and Parameter Counting in MicroTorch
Building PyTorch: A Hands-On Guide to the Core Foundations of a Training Framework
12:38
Building PyTorch: A Hands-On Guide to the Core Foundations of a Training Framework
Building PyTorch from Scratch: Tensor Operations, Transpose & Backward Method Explained
17:27
Building PyTorch from Scratch: Tensor Operations, Transpose & Backward Method Explained
Building PyTorch from Scratch: Create a Tensor Class with AutoDiff
18:14
Building PyTorch from Scratch: Create a Tensor Class with AutoDiff
Neural Networks from Scratch: Building a Fashion MNIST Classifier with Softmax and Cross-Entropy
29:42
Neural Networks from Scratch: Building a Fashion MNIST Classifier with Softmax and Cross-Entropy
Fixing Unstable Gradient Descent: Momentum and Gradient Clipping in Deep Learning
15:41
Fixing Unstable Gradient Descent: Momentum and Gradient Clipping in Deep Learning
The Perceptron - one of the first Neural Networks
1:47
The Perceptron - one of the first Neural Networks
Landscape of the Ackley Function
0:11
Landscape of the Ackley Function
Linear Separability: OR vs XOR
1:39
Linear Separability: OR vs XOR
Exploding Gradient Problem
0:19
Exploding Gradient Problem
Deep Neural Network: The Training Process with animated decision boundaries
0:31
Deep Neural Network: The Training Process with animated decision boundaries
Building a Deep Neural Network from Scratch: Solving the Non-Linear Problem
19:17
Building a Deep Neural Network from Scratch: Solving the Non-Linear Problem
Building Neural Networks from Scratch: Implementing Linear Layer and Stochastic Gradient Descent
32:44
Building Neural Networks from Scratch: Implementing Linear Layer and Stochastic Gradient Descent
Understanding Weight Initialization in Neural Networks: Normal, Xavier, He, and Leaky He
18:24
Understanding Weight Initialization in Neural Networks: Normal, Xavier, He, and Leaky He
Understanding Logits to Probabilities: How Neural Networks Make Decisions with Sigmoid Function
12:44
Understanding Logits to Probabilities: How Neural Networks Make Decisions with Sigmoid Function
Binary Cross-Entropy Loss Explained: A Complete Visual Guide
22:44
Binary Cross-Entropy Loss Explained: A Complete Visual Guide
Gradient Descent: Unlocking Global Minima with Momentum
21:28
Gradient Descent: Unlocking Global Minima with Momentum
3D Gradient Descent: The Rollercoaster of Optimization
19:32
3D Gradient Descent: The Rollercoaster of Optimization
Gradient Descent: Downhill to the Minima. A Visual Journey to Optimization
25:50
Gradient Descent: Downhill to the Minima. A Visual Journey to Optimization