Machine Learning & Deep Learning

9 min read

Introduction

Artificial intelligence encompasses the broad goal of creating intelligent systems.

Machine Learning in 30 Seconds

Machine learning is a simple idea: instead of programming rules, you give the computer examples and let it figure out the rules itself.

  1. Collect data (inputs and expected outputs)
  2. Choose a model architecture
  3. Train: adjust model parameters until it makes good predictions
  4. Use the trained model on new, unseen inputs

The “learning” is the process of adjusting parameters to minimize errors — a process called optimization.

Machine Learning (ML) and Deep Learning (DL) represent two interconnected but distinct approaches to artificial intelligence, with deep learning functioning as an advanced subset of machine learning. While both enable systems to learn from data, they differ fundamentally in their approach to problem-solving, data requirements, computational needs, and application domains. Understanding these distinctions is essential for engineers and developers making architectural decisions about which technology to deploy.

Traditional ML models find patterns through explicit algorithms (decision trees split on features; logistic regression fits a decision boundary). Deep learning uses back-propagation. This iterative refinement mirrors how humans learn through feedback. The beauty—and challenge—lies in that the network autonomously decides which features to emphasize. No human intervention required.

Conceptual Foundation

Machine learning narrows this scope to systems that learn patterns from data, rather than following explicitly programmed instructions. Deep learning represents a further specialization—a sophisticated subset of machine learning that leverages neural networks with multiple layers to automatically discover the representations needed for detection or classification from raw input.

Think of machine learning as teaching a system through examples and labeled data, where you curate which features matter. Deep learning takes a different approach: you feed raw data directly into a multi-layered neural network, which progressively learns increasingly abstract representations. In web development terms, if machine learning is like building a feature extraction module yourself, deep learning is like delegating that entire task to an intelligent system that figures out what matters on its own.

Computational, Hardware and Time Requirements

Machine learning models typically train on standard CPUs with reasonable memory requirements. A laptop or modest server suffices for most applications. Machine learning models train relatively quickly—often minutes to hours even for complex problems.

Deep learning’s complexity necessitates specialized hardware. Training deep networks requires GPUs (Graphics Processing Units) or TPUs (Tensor Processing Units) for accelerated parallel computation. A single modern deep learning model may consume weeks of GPU time and terabytes of memory. This hardware requirement fundamentally impacts deployment decisions and operational costs. Deep learning training can span days, weeks, or longer depending on dataset size, model architecture, and hardware available. This temporal investment influences development cycles and experimentation velocity.

Interpretability and Explainability

Machine learning models are generally interpretable. You can inspect which features the model weighted heavily, examine decision trees visually, or understand linear regression coefficients directly. This transparency is critical in regulated domains like finance and healthcare, where stakeholders must justify model decisions.

Deep learning models are notoriously difficult to interpret—the “black box” problem. With millions of parameters distributed across layers, tracing why a network made a specific decision is mathematically complex. Recent work in interpretability (attention visualization, gradient-based explanations) has improved this situation, but deep learning will never match traditional ML’s transparency.

Transfer Learning

If a model is trained on a large and general enough dataset, this model will effectively serve as a generic model of the visual world. You can then take advantage of these learned feature maps without having to start from scratch by training a large model on a large dataset.

Latent Space

In machine learning (ML) is a compressed representation of data points that preserves only essential features that inform the input data’s underlying structure. Effectively modeling latent space is an integral part of deep learning, including most generative AI (gen AI) algorithms.

From ML to Deep Learning

Traditional ML models (linear regression, decision trees, SVMs) work well but struggle with complex, high-dimensional data like images, text, and audio.

Deep learning uses neural networks with many layers — “deep” networks — that can learn hierarchical representations. Each layer learns progressively more abstract features:

  • Layer 1: edges and simple shapes
  • Layer 2: textures and patterns
  • Layer 3: object parts
  • Layer 4: whole objects

This hierarchy is why deep learning dominates vision, language, and audio tasks.

The Training Loop

Every deep learning model follows the same basic training loop:

  1. Forward pass: Feed input through the network, get a prediction
  2. Loss calculation: Compare prediction to the expected output
  3. Backward pass: Calculate how to adjust each parameter to reduce the loss
  4. Update: Adjust parameters slightly in the right direction

Repeat millions of times with different examples. This process is called gradient descent — you’re descending the “landscape” of possible parameter values, looking for the lowest error.

Why This Matters for Generative AI

Generative AI models are deep learning models trained on a specific task: predict the next token. The training data is vast (large chunks of the internet), the model is enormous (billions of parameters), and the training process is expensive (millions of dollars in compute).

But the core loop is the same: forward pass, compute loss, backpropagate, update. The magic is in the scale, the data, and the architecture — which we’ll explore next.