Published: October 26, 2023

Deep Learning Basics

Deep learning is a subfield of machine learning that focuses on algorithms inspired by the structure and function of the human brain. It's been incredibly successful in areas like image recognition, natural language processing, and speech recognition. This post will cover the fundamental concepts.

Key Concepts

Neural Networks: The core building blocks of deep learning. They consist of interconnected nodes (neurons) organized in layers.
Layers: Deep learning models often have multiple layers – input, hidden, and output.
Backpropagation: The algorithm used to train neural networks by adjusting the weights based on the error.
Activation Functions: Non-linear functions applied to the output of each neuron.

A Simple Example (Python)

            
                import numpy as np

                # Example: Simple linear regression
                X = np.array([[1], [2], [3]])
                y = np.array([2, 4, 6])

                # Using numpy's linear algebra functions
                from numpy import linalg

                w = linalg.solve(w.T @ w, w.T @ y)

                print(f"Weight: {w}")

This is a very basic example, but it demonstrates how deep learning models learn patterns from data.

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