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Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction

Towards AI

This ability empowers them to identify patterns, make predictions, and even generate creative content. Slope, intercept in case of linear regression). Linear Regression Decision Trees Support Vector Machines Neural Networks Clustering Algorithms (e.g., You show many cats to the child.

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Unsupervised Learning: Discovering Hidden Patterns and Insights

Artificial Corner

Recommendation systems: Streaming platforms leverage unsupervised learning to analyze users’ past choices and recommend personalized content, such as movies or music, enhancing the overall user experience. Stay tuned for more exciting content! Thank you for joining me on this journey of understanding unsupervised learning.

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Discovering the Technology Behind Photo Tagging Systems

Artificial Corner

Machine Learning System Design Series Learn how a photo tagging system, like the one used by Facebook, works in detail. Facebook tag suggestion system — Source: [link] No matter how old you are, I bet you have crossed Facebook or Instagram once. The core of our solution is a system that suggests photo tags based on who’s in them.

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Learning JAX in 2023: Part 3 — A Step-by-Step Guide to Training Your First Machine Learning Model with JAX

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Home Table of Contents Learning JAX in 2023: Part 3 — A Step-by-Step Guide to Training Your First Machine Learning Model with JAX Configuring Your Development Environment Having Problems Configuring Your Development Environment? ? Train a Simple Model with JAX Build a Linear Dataset Linear Model Build a Nonlinear Dataset Nonlinear Model ?

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Interfaces for Explaining Transformer Language Models

Jay Alammar

This article focuses on auto-regressive models, but these methods are applicable to other architectures and tasks as well. It is a linear transformation of 5,449 neurons (30% of the 18,432 neurons in the FFNN layers: 3072 per layer, 6 layers in DistilGPT2). It is a linear transformation of 8,542 neurons (46% of the FFNN neurons).