ML Fundamentals

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Deep Learning for Newbies: Supervised or Unsupervised? 🛤️🧠 - Topic 013 #ai #ml

Telematics with Metaflow: How Nirvana Insurance built a large-scale Risk Estimation platform

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Trying Beaded Jump Rope with Mandy + Harvestmen 🕷️

Lightning Talk: Triton Compiler - Thomas Raoux, OpenAI

Lightning Talk: Streamlining Model Export with the New ONNX Exporter - Maanav Dalal & Aaron Bockover

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Artificial Neural Network Tutorial | Deep Learning With Neural Networks | Edureka Rewind

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This is the Math You Need to Master Reinforcement Learning

Bellman Equation - Explained!

Lecture 1 Part 1: Introduction and Motivation

Lecture 8 Part 1: Derivatives of Eigenproblems

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Adversarial Attacks and Defenses. The Dimpled Manifold Hypothesis. David Stutz from DeepMind #HLF23

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The AI World Cup | Scoring Goals with Artificial Intelligence | Abhay Sharma

Lightning Talk: Accelerated Inference in PyTorch 2.X with Torch...- George Stefanakis & Dheeraj Peri

Lightning Talk: Accelerating PyTorch Performance with OpenVINO - Yamini, Devang & Mustafa

Lightning Talk: Uplink Interference Optimizer, How to Optimize a Cellular Network...- Oscar Gonzalez

Keynote: Intel and PyTorch: Enabling AI Everywhere with Ubiquitous Hardware and Open... - Fan Zhao

Lightning Talk: A Novel Domain Generalization Technique for Medical Imaging Using... - Dinkar Juyal

Lightning Talk: Orchestrating Machine Learning on Edge Devices...- Rishit Dagli & Shivay Lamba

Lightning Talk: Seismic Data to Subsurface Models with OpenFWI - Benjamin Consolvo, Intel

Lightning Talk: Energy-Efficient Deep Learning with PyTorch and Zeus - Jae-Won Chung

Lecture 3 Part 1: Kronecker Products and Jacobians

Lecture 3 Part 2: Finite-Difference Approximations

Lecture 2 Part 1: Derivatives in Higher Dimensions: Jacobians and Matrix Functions

Lecture 4 Part 2: Nonlinear Root Finding, Optimization, and Adjoint Gradient Methods

Lecture 7 Part 2: Second Derivatives, Bilinear Forms, and Hessian Matrices

Lecture 4 Part 1: Gradients and Inner Products in Other Vector Spaces

Lecture 2 Part 2: Vectorization of Matrix Functions

Lecture 1 Part 2: Derivatives as Linear Operators

Lecture 8 Part 2: Automatic Differentiation on Computational Graphs

Lecture 7 Part 1: Derivatives of Random Functions

Lecture 6 Part 1: Adjoint Differentiation of ODE Solutions

Lecture 6 Part 2: Calculus of Variations and Gradients of Functionals

Supervised Learning in Neural Networks: An Explainer 🧠🔍 - Topic 012 #ai #ml

How Many Pushups to Failure?? with Mandy 🥵

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How Neural Networks Learn: A Workplace Analogy 🏢🧠 - Topic 011 #ai #ml

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Types Of Artificial Intelligence | Artificial Intelligence Explained | What is AI? | Edureka

Deep Learning 101: Training, Goals, and Predictions Explained 🚀📘 - Topic 010 #ai #ml