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📐 ML Fundamentals

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

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Bellman Equation -  Explained!
ML Fundamentals
Bellman Equation - Explained!
CodeEmporium Advanced 2y ago
How to solve problems with Reinforcement Learning | Markov Decision Process
ML Fundamentals
How to solve problems with Reinforcement Learning | Markov Decision Process
CodeEmporium Advanced 2y ago
Why Deep Networks and Brains Learn Similar Features with Sophia Sanborn - 644
ML Fundamentals
Why Deep Networks and Brains Learn Similar Features with Sophia Sanborn - 644
The TWIML AI Podcast with Sam Charrington Advanced 2y ago
Stanford XCS224U I Analysis NLU, Pt 4: Casual Abstraction & Interchange Intervention Training (IIT)
ML Fundamentals
Stanford XCS224U I Analysis NLU, Pt 4: Casual Abstraction & Interchange Intervention Training (IIT)
Stanford Online Advanced 2y ago
Designing Human in the Loop Experiences for LLMs // Alberto Rizzoli // LLMs in Prod Con Part 2
ML Fundamentals
Designing Human in the Loop Experiences for LLMs // Alberto Rizzoli // LLMs in Prod Con Part 2
MLOps.community Advanced 2y ago
How to Hack an AI
ML Fundamentals
How to Hack an AI
SANS Institute Advanced 2y ago
The secret to Accelerate #ML and #GenerativeAI: ML Developer Tools from Weights & Biases.
ML Fundamentals
The secret to Accelerate #ML and #GenerativeAI: ML Developer Tools from Weights & Biases.
Google Cloud Advanced 2y ago
A pretty reason why Gaussian + Gaussian = Gaussian
ML Fundamentals
A pretty reason why Gaussian + Gaussian = Gaussian
3Blue1Brown Advanced 2y ago
Day in the life of Oxford Advanced Management & Leadership Programme - June 2023
ML Fundamentals
Day in the life of Oxford Advanced Management & Leadership Programme - June 2023
Saïd Business School, University of Oxford Advanced 2y ago
In-Context Learning Over Graphs for LLMs: PRODIGY (Stanford)
ML Fundamentals
In-Context Learning Over Graphs for LLMs: PRODIGY (Stanford)
Discover AI Advanced 2y ago
Stanford Seminar - Control-Oriented Learning for Dynamical Systems
ML Fundamentals
Stanford Seminar - Control-Oriented Learning for Dynamical Systems
Stanford Online Advanced 2y ago
PyTorch New York Meetup - June 2023
ML Fundamentals
PyTorch New York Meetup - June 2023
PyTorch Advanced 2y ago
Hugging Face: Fine-Tune NLP Pipeline for Question Answering | Transformers & Attention Mechanism
ML Fundamentals
Hugging Face: Fine-Tune NLP Pipeline for Question Answering | Transformers & Attention Mechanism
Analytics Vidhya Advanced 2y ago
Deep Learning for Computer Vision with Python and TensorFlow – Complete Course
ML Fundamentals
Deep Learning for Computer Vision with Python and TensorFlow – Complete Course
freeCodeCamp.org Advanced 2y ago
Sloth or Pastry? Using PyTorch and Deep Learning for Image Classification
ML Fundamentals
Sloth or Pastry? Using PyTorch and Deep Learning for Image Classification
DataCamp Advanced 2y ago
The importance of hyperparameter optimization
ML Fundamentals
The importance of hyperparameter optimization
The TWIML AI Podcast with Sam Charrington Advanced 2y ago
ML Evolution & Recurring Themes // Waleed Kadous // MLOps Podcast # 155 short clip
ML Fundamentals
ML Evolution & Recurring Themes // Waleed Kadous // MLOps Podcast # 155 short clip
MLOps.community Advanced 2y ago
The secret sauce to creating amazing ML experiences for developers
ML Fundamentals
The secret sauce to creating amazing ML experiences for developers
TensorFlow Advanced 3y ago
Image Editing A.I.
ML Fundamentals
Image Editing A.I.
sentdex Advanced 3y ago
6.1: Using models from Python in the web browser with TensorFlow.js
ML Fundamentals
6.1: Using models from Python in the web browser with TensorFlow.js
Google for Developers Advanced 3y ago
Ready for the Mathematics for Machine Learning and Data Science Specialization? 🚀
ML Fundamentals
Ready for the Mathematics for Machine Learning and Data Science Specialization? 🚀
DeepLearningAI Advanced 3y ago
3.6.2: Using advanced pre-trained Web ML models - Part 2: Use MoveNet for pose estimation in browser
ML Fundamentals
3.6.2: Using advanced pre-trained Web ML models - Part 2: Use MoveNet for pose estimation in browser
Google for Developers Advanced 3y ago
MSR-IISc AI Seminar Series: On Learning-Aware Mechanism Design - Michael I. Jordan
ML Fundamentals
MSR-IISc AI Seminar Series: On Learning-Aware Mechanism Design - Michael I. Jordan
Microsoft Research Advanced 3y ago
Food for Diffusion
ML Fundamentals
Food for Diffusion
HuggingFace Advanced 3y ago
Understanding and Avoiding Data Leakage with Hamel Husain
ML Fundamentals
Understanding and Avoiding Data Leakage with Hamel Husain
Weights & Biases Advanced 3y ago
Pythae: Unifying Generative Autoencoder Implementations in PyTorch
ML Fundamentals
Pythae: Unifying Generative Autoencoder Implementations in PyTorch
PyTorch Advanced 3y ago
Machine Learning for Combinatorial Optimization: Some Empirical Studies
ML Fundamentals
Machine Learning for Combinatorial Optimization: Some Empirical Studies
Microsoft Research Advanced 3y ago
Multi Armed Bandits - Reinforcement Learning Explained!
ML Fundamentals
Multi Armed Bandits - Reinforcement Learning Explained!
CodeEmporium Advanced 2y ago
Elements of Reinforcement Learning
ML Fundamentals
Elements of Reinforcement Learning
CodeEmporium Advanced 2y ago
ChatGPT: Zero to Hero
ML Fundamentals
ChatGPT: Zero to Hero
CodeEmporium Advanced 2y ago
Sentence Embeddings - EXPLAINED!
ML Fundamentals
Sentence Embeddings - EXPLAINED!
CodeEmporium Advanced 2y ago
Word2Vec, GloVe, FastText- EXPLAINED!
ML Fundamentals
Word2Vec, GloVe, FastText- EXPLAINED!
CodeEmporium Advanced 2y ago
Stanford Seminar - A Picture of the Prediction Space of Deep Networks
ML Fundamentals
Stanford Seminar - A Picture of the Prediction Space of Deep Networks
Stanford Online Advanced 2y ago
Stanford Seminar - Human-AI Interaction Under Societal Disagreement
ML Fundamentals
Stanford Seminar - Human-AI Interaction Under Societal Disagreement
Stanford Online Advanced 2y ago
Stanford Seminar - Connecting Robotics and Foundation Models, Brian Ichter of Google DeepMind
ML Fundamentals
Stanford Seminar - Connecting Robotics and Foundation Models, Brian Ichter of Google DeepMind
Stanford Online Advanced 2y ago
Stanford CS330 I Advanced Meta-Learning 2: Large-Scale Meta-Optimization l 2022 I Lecture 10
ML Fundamentals
Stanford CS330 I Advanced Meta-Learning 2: Large-Scale Meta-Optimization l 2022 I Lecture 10
Stanford Online Advanced 3y ago
Sentence Tokenization in Transformer Code from scratch!
ML Fundamentals
Sentence Tokenization in Transformer Code from scratch!
CodeEmporium Advanced 3y ago
Blowing up Transformer Decoder architecture
ML Fundamentals
Blowing up Transformer Decoder architecture
CodeEmporium Advanced 3y ago
Blowing up the Transformer Encoder!
ML Fundamentals
Blowing up the Transformer Encoder!
CodeEmporium Advanced 3y ago
Layer Normalization - EXPLAINED (in Transformer Neural Networks)
ML Fundamentals
Layer Normalization - EXPLAINED (in Transformer Neural Networks)
CodeEmporium Advanced 3y ago
Positional Encoding in Transformer Neural Networks Explained
ML Fundamentals
Positional Encoding in Transformer Neural Networks Explained
CodeEmporium Advanced 3y ago
GPT - Explained!
ML Fundamentals
GPT - Explained!
CodeEmporium Advanced 3y ago
What does GPT in ChatGPT do?
ML Fundamentals
What does GPT in ChatGPT do?
CodeEmporium Advanced 3y ago
1.3: Breakdown of WebML course
ML Fundamentals
1.3: Breakdown of WebML course
Google for Developers Advanced 3y ago
How web developers can use machine learning
ML Fundamentals
How web developers can use machine learning
Google for Developers Advanced 3y ago
ChatGPT and Reinforcement Learning
ML Fundamentals
ChatGPT and Reinforcement Learning
CodeEmporium Advanced 3y ago
Chat GPT Rewards Model Explained!
ML Fundamentals
Chat GPT Rewards Model Explained!
CodeEmporium Advanced 3y ago
Machine learning assisted hyper-heuristics for online combinatorial optimization problems
ML Fundamentals
Machine learning assisted hyper-heuristics for online combinatorial optimization problems
Microsoft Research Advanced 3y ago
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Precalculus: Mathematical Modeling
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Precalculus: Mathematical Modeling
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Introduction to RNN and DNN
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Introduction to RNN and DNN
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Foundations of Data Science and Machine Learning with Python
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Foundations of Data Science and Machine Learning with Python
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Interpretable Machine Learning Applications: Part 1
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Interpretable Machine Learning Applications: Part 1
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Modelos predictivos con Machine Learning
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Modelos predictivos con Machine Learning
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Production Machine Learning Systems - Français
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Production Machine Learning Systems - Français
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