The source provides an extensive overview of machine learning fundamentals tailored for AI engineers.
Intelligence and World Models
Explains how intelligence relies on building and understanding world models, and how computers can learn these models using machine learning techniques.
Learning Approaches
Machine Learning (ML): Describes how computers learn patterns and models from data, covering the essential phases of training (fitting models to data) and inference (using trained models for predictions).
Deep Learning (DL): Focuses on neural networks, explaining their inner workings, including neurons, layers, and various network architectures (such as convolutional neural networks and transformers).
Reinforcement Learning (RL): Outlines how models improve by trial and error, aiming to maximize a reward function as they interact with an environment.
Traditional Machine Learning Techniques
Covers popular methods such as linear regression (for predicting continuous values) and logistic regression (for binary classification), among other classic algorithms.
Deep Learning Details
Neural Networks: Discusses components like neurons (which process inputs and outputs), layers (which build complex models), and various architectures.
Gradient Descent: Explains the iterative algorithm for training models by minimizing error (loss functions).
Hyperparameters: Lists important settings like learning rate, batch size, dropout, and epochs that guide the training process.
Reinforcement Learning
Highlights the process of learning through trial and error, where models adjust their actions to maximize cumulative rewards over time.
Data Quality and Quantity
Concludes by stressing that data quality and quantity are fundamental for developing effective machine learning models, as they form the basis for reliable learning and generalization.
Main and Interesting Concepts with Examples
Intelligence and World Models
Concept: Intelligence requires understanding how the world works, so we develop "models of the world" to compress complex reality into something comprehensible. These models help us make predictions and achieve outcomes. Humans and computers both do this.
Example: Seeing dark clouds, your mental model lets you predict it's going to rain later.
Learning (for Computers)
Concept: For computers, "learning" means performing tasks or making decisions without explicit step-by-step instructions—unlike traditional software.
Example: Instead of a programmer coding every specific action, a computer learns from data and performs tasks based on patterns it discovers.
Machine Learning (ML)
Concept: ML lets computers learn tasks directly from data in two phases: training and inference. Model parameters are fit to real-world data using math. Traditional ML often requires selecting relevant input variables (feature engineering).
Example:
Linear Model: Predicting tomorrow's temperature (Y) based on today's (X) with parameters M and B: Y = M*X + B.
Training Phase: Collecting data to find optimal M and B, minimizing error between predicted and actual Y.
Inference Phase: Using the trained model to make predictions for new X values.
Feature Engineering: Choosing "today's temperature" as a predictor is better than irrelevant variables like "number of cappuccinos drank".
Other techniques: logistic regression (classification), decision trees, support vector machines.
Loss Function
Concept: Quantifies discrepancy between predictions and real-world data during training. The goal is to minimize the loss by adjusting model parameters.
Example: When predicting tomorrow's temperature, loss function measures the difference between actual records (Y) and predictions (M*X + B).
Deep Learning (DL)
Concept: A type of ML with neural networks that learn optimal features automatically, reducing manual feature engineering. Neural networks can approximate almost any function through a series of operations.
Example:
Image Classification: Neural networks learn to classify images from raw pixels, progressing from edges (early layers), to features like eyes (mid layers), to full objects (late layers).
Neural Building Blocks: A neuron takes inputs, multiplies by weights, sums, adds bias, and passes through an activation like ReLU or Sigmoid.
Network Architectures: CNNs for images; Transformers (attention layers) revolutionize NLP and language models.
Gradient Descent
Concept: An algorithm to train neural nets by iteratively updating parameters to minimize loss. It computes the gradient (steepest ascent), then moves opposite—towards minimum loss. The learning rate controls step size.
Example: In a bumpy landscape (loss function), gradient tells which way is up; gradient descent walks you down to lowest point (minimum loss). Variants include stochastic gradient descent (one datapoint), mini-batch, and Adam (with momentum).
Hyperparameters
Concept: Values guiding training, set by developer (not learned from data).
Example:
Epochs: Number of times optimizer passes through entire dataset.
Learning Rate: Step size during optimization.
Batch Size: Number of samples per update step.
Dropout: Fraction of neurons randomly set to zero while training (for robustness, reducing overfitting).
Reinforcement Learning (RL)
Concept: Lets computers learn by trial and error. Models take actions, get rewards/penalties, and adjust parameters to maximize total reward. Unbound by human labeling, RL can exceed human expertise and aims to maximize reward function (not minimize loss).
Example:
AlphaGo: DeepMind's AlphaGo learned Go by playing itself and receiving rewards for wins—developing novel strategies that surpassed human grandmasters.
DeepSeek R1: Used correctness of math/coding solutions as reward.
Gradient Ascent: RL algorithms like Reinforce add the gradient to parameters to maximize rewards.
Data Quality and Quantity
Concept: Data is the most important ingredient; bad data cannot be overcome by good algorithms ("garbage in, garbage out"). Good data means enough (quantity) and correct/diverse (quality).
Example:
Quantity: More data helps generalization; too little data can lead to overfitting.
Quality - Accuracy: Data errors (like wrong age or income) reduce effectiveness.
Quality - Diversity: Training only on "pro" users makes models less useful for "enterprise" users; data must represent all relevant scenarios.