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662 lessons

📐 ML Fundamentals

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

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Class 28 Video: Feature Extraction and Machine Learning (II)
📐 ML Fundamentals
Class 28 Video: Feature Extraction and Machine Learning (II)
MIT OpenCourseWare Advanced 5mo ago
Dataflow Computing for AI Inference [Kunle Olukotun] - 751
📐 ML Fundamentals
Dataflow Computing for AI Inference [Kunle Olukotun] - 751
TWIML AI Podcast Advanced 5mo ago
Mimecast’s AI solution helps mitigate human risk with AWS | Amazon Web Services
📐 ML Fundamentals
Mimecast’s AI solution helps mitigate human risk with AWS | Amazon Web Services
Amazon Web Services Advanced 5mo ago
VGGNet - Explained!
📐 ML Fundamentals
VGGNet - Explained!
CodeEmporium Advanced 5mo ago
Text Classification: Handling 1000+ Classes with Generative AI | Amazon Web Services
📐 ML Fundamentals
Text Classification: Handling 1000+ Classes with Generative AI | Amazon Web Services
Amazon Web Services Advanced 5mo ago
Inception Net - Explained! (with code)
📐 ML Fundamentals
Inception Net - Explained! (with code)
CodeEmporium Advanced 5mo ago
Stanford Webinar - From Pixels to Human Impact: Unlocking Real-World Value with Computer Vision
📐 ML Fundamentals
Stanford Webinar - From Pixels to Human Impact: Unlocking Real-World Value with Computer Vision
Stanford Online Advanced 6mo ago
Pointwise Convolutions - EXPLAINED (with code)
📐 ML Fundamentals
Pointwise Convolutions - EXPLAINED (with code)
CodeEmporium Advanced 6mo ago
Future Climate Innovators Summer School
📐 ML Fundamentals
Future Climate Innovators Summer School
Saïd Business School, University of Oxford Advanced 6mo ago
Will AI Replace Your Job? Here’s the Truth #FutureOfWork #AIJobs #Workplace #Automation #AIFuture
📐 ML Fundamentals
Will AI Replace Your Job? Here’s the Truth #FutureOfWork #AIJobs #Workplace #Automation #AIFuture
Data Science Dojo Advanced 6mo ago
Deconvolution - what do networks learn? (visualization + code)
📐 ML Fundamentals
Deconvolution - what do networks learn? (visualization + code)
CodeEmporium Advanced 6mo ago
How you can SAVE A LOT OF TIME with Pre-trained models
📐 ML Fundamentals
How you can SAVE A LOT OF TIME with Pre-trained models
DataCamp Advanced 6mo ago
Why synthetic data isn’t enough
📐 ML Fundamentals
Why synthetic data isn’t enough
Weights & Biases Advanced 6mo ago
Machine Learning models are HERE TO STAY
📐 ML Fundamentals
Machine Learning models are HERE TO STAY
DataCamp Advanced 6mo ago
The AI REVOLUTION has TAKEN US by STORM
📐 ML Fundamentals
The AI REVOLUTION has TAKEN US by STORM
DataCamp Advanced 6mo ago
The SURPRISING POWER of PRIVATE DATA Revealed
📐 ML Fundamentals
The SURPRISING POWER of PRIVATE DATA Revealed
DataCamp Advanced 6mo ago
R-CNN - Explained!
📐 ML Fundamentals
R-CNN - Explained!
CodeEmporium Advanced 6mo ago
Region Proposals - Explained!
📐 ML Fundamentals
Region Proposals - Explained!
CodeEmporium Advanced 7mo ago
Eco-aware GNN Recommenders
📐 ML Fundamentals
Eco-aware GNN Recommenders
Data Skeptic Advanced 7mo ago
Broadridge Forecasts the Future of Asset Management with AWS | Amazon Web Services
📐 ML Fundamentals
Broadridge Forecasts the Future of Asset Management with AWS | Amazon Web Services
Amazon Web Services Advanced 7mo ago
Image segmentation - Explained!
📐 ML Fundamentals
Image segmentation - Explained!
CodeEmporium Advanced 7mo ago
How was object detection done before neural networks?
📐 ML Fundamentals
How was object detection done before neural networks?
CodeEmporium Advanced 7mo ago
Tom Jacobs Weight Decay Controls Implicit Regularization Insights on Generalization and Sparsity
📐 ML Fundamentals
Tom Jacobs Weight Decay Controls Implicit Regularization Insights on Generalization and Sparsity
Cohere Advanced 7mo ago
Mufan Li - Introduction to Neural Network Scaling Limits
📐 ML Fundamentals
Mufan Li - Introduction to Neural Network Scaling Limits
Cohere Advanced 7mo ago