17 docs tagged with "Machine Learning"

These three terms are often used interchangeably, but they refer to different concepts with varying levels of complexity and capability.

Binary Classification

Broadcasting is a powerful feature in NumPy (and many deep learning libraries) that lets you perform operations on arrays of different shapes without writing explicit for loops. This makes your code simpler, faster, and more readable.

🧠 What is a Computation Graph?

This notebook explains how to compute derivatives using a computation graph, following the example from Andrew Ng's neural networks course. It breaks down a simple function into smaller steps and shows how to apply the chain rule to calculate how changes in inputs affect the final output.

What is Logistic Regression?

What is this about?

This page explains how to compute derivatives in logistic regression using a computation graph, broken down into easy-to-understand steps.

Understanding Machine Learning Limitations, Feedback Loops, and Classification vs. Regression

🟢 Forward Propagation (Forward Pass)

🌈 The Function: f(x) = x^2

Overview of Model Training

This knowledge item (KI) explains how we move from a loop-based implementation of logistic regression to a clean, fast, vectorized version using NumPy.

In machine learning, weights are crucial parameters that are learned during the training of a model, such as a neural network. They help the model make predictions by assigning importance to the inputs it receives. Let's break down the concept further:

Understanding Neural Networks, Weights, and Stochastic Gradient Descent

Machine learning is a field of artificial intelligence (AI) that focuses on creating algorithms and models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed for every specific task. The goal is to allow systems to automatically improve their performance as they are exposed to more data over time.

What is Object Detection?