📐 ML Fundamentals

Neural networks, backpropagation, gradient descent — the maths behind AI

ML Fundamentals

Leading consultant on how machine learning works and how to successfully operationalize it

Talks at Google Beginner 1y ago

ML Fundamentals

Daniela Rus & Gregory Mone | The Heart and the Chip: Our Bright Future with Robots | Talks at Google

Talks at Google Beginner 2y ago

ML Fundamentals

Juan Corvalan | Incubating Predictive Systems: Application Challenges | Talks at Google

Talks at Google Beginner 3y ago

ML Fundamentals

NASA Chief Scientist | Dr. Jim Green | Talks at Google

Talks at Google Beginner 6y ago

ML Fundamentals

Rage Inside The Machine | Robert Elliott Smith | Talks at Google

Talks at Google Beginner 6y ago

ML Fundamentals

AI Governance & Risk Management | Kartik Hosanagar | Talks at Google

Talks at Google Beginner 6y ago

ML Fundamentals

Behind The Scenes: Cirque du Soleil's 'VOLTA' | Ron Kellum & Bernard Petiot | Talks at Google

Talks at Google Beginner 7y ago

ML Fundamentals

Future Politics | Jamie Susskind | Talks at Google

Talks at Google Beginner 7y ago

ML Fundamentals

Quantum Computing, AI and Blockchain: The Future of IT | Shoucheng Zhang | Talks at Google

Talks at Google Beginner 7y ago

ML Fundamentals

Hunting for Extraterrestrial Life | Dr. Penelope Boston | Talks at Google

Talks at Google Beginner 8y ago

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K-Means Clustering 101: World Happiness Report

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Reinforcement Learning for Trading Strategies

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Machine Learning Essentials for Business and Technical Decision Makers

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Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation - Bahasa Indonesia

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Supervised Text Classification for Marketing Analytics

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Build Image Quality Inspection using AWS Lookout for Vision

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Tools for Data Science

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Python Fundamentals and Data Science Essentials

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Introduction to Image Generation - Deutsch

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Deep Learning: Build & Optimize Neural Networks

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Linear Algebra: Linear Systems and Matrix Equations

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Machine Learning with Implementation in Java

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Deep Learning with PyTorch

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Machine Learning for Engineers: Algorithms and Applications

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Foundations of AI Engineering

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Trees, SVM and Unsupervised Learning

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Linear Algebra: Orthogonality and Diagonalization

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Data for Machine Learning

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