Information Theory From Scratch (Full Course) — Bits, Entropy & Channels

Computer Vision From Scratch — Full Course (Pixels to Vision Transformers)

Computer Vision #11: ImageNet & the Deep Learning Revolution

Computer Vision #12: How CNNs Learn (Training)

Computer Vision #10: The Feature Hierarchy

Computer Vision #9: Anatomy of a CNN

Computer Vision #8: Why We Need CNNs

Computer Vision #7: Handcrafted Features — SIFT & HOG

Computer Vision #6: Edge Detection

Computer Vision #5: Convolution — The One Idea That Matters

Deep Learning Part 8: Attention & the Transformer

Computer Vision #4: Image Filters (Classical Vision)

Computer Vision #3: The Tasks of Computer Vision

Computer Vision #2: Pixels — How Computers Store Images

Deep Learning Foundations — Full Course (From a Single Neuron to the Transformer)

Computer Vision #1: What Computer Vision Really Is

How LLMs Actually Work — Build a Transformer From Scratch (Full Course, Visualized)

Deep Learning Part 7: Convolutional Neural Networks (CNNs)

Deep Learning Part 6: How to Actually Train Deep Networks

Deep Learning Part 5: Activation Functions & Nonlinearity

Deep Learning Part 4: Backpropagation, Clearly Explained

Deep Learning Part 3: Loss Functions & Gradient Descent

Deep Learning Part 2: The Neuron & Forward Propagation

Deep Learning Part 1: What a Neural Network Really Is

EM Behavioral Interview Part 5: New Teams, PM Conflict, Mentorship & Feedback

EM Behavioral Interview Part 4: Remote Teams, Inclusion, Reliability & Strategy

EM Behavioral Interview Part 3: Managing Up, Driving Change & Hard Calls

EM Behavioral Interview Part 2: Team Performance, Morale & Retention

Engineering Manager Behavioral Interview: 15 Questions & How to Answer (Full Guide)

Math for ML #38: The Complete Picture

Math for ML #37: Regularization — L1 & L2

Math for ML #36: The Jacobian Matrix

Math for ML #35: Taylor Series

Math for ML #34: Markov Chains

Math for ML #33: Precision & Recall

Math for ML #32: KL Divergence

Math for ML #31: Sigmoid & Softmax

Math for ML #30: Hypothesis Testing & P-Values

Math for ML #29: Entropy & Cross-Entropy Loss

Math for ML #28: Maximum Likelihood Estimation

Math for ML #27: Expectation & Variance

Math for ML #26: Convexity & Optimization Landscapes

Math for ML #25: Logarithms & Exponentials

Math for ML #24: Span, Basis & Dimension

Math for ML #23: Linear Systems — Ax = b

Math for ML #22: Linear Regression & Least Squares

Math for ML #21: Statistics — Mean, Variance & Correlation

Math for ML #20: Bayes' Theorem

Math for ML #19: Distributions & The Bell Curve

Math for ML #18: Probability Foundations

Math for ML #17: Integrals — Area & Accumulation

Math for ML #16: Gradient Descent

Math for ML #15: Gradients & Partial Derivatives

Math for ML #14: The Chain Rule

Math for ML #13: The Derivative

Math for ML #12: Sine, Cosine & Periodic Functions

Math for ML #11: Angles & The Unit Circle

Math for ML #10: Lines, Planes & Hyperplanes

Math for ML #9: Distance, Norms & Geometry

Math for ML #8: Eigenvectors & Eigenvalues