📐 ML Fundamentals

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

ML Fundamentals

The SURPRISING POWER of PRIVATE DATA Revealed

DataCamp Advanced 7mo ago

ML Fundamentals

R-CNN - Explained!

CodeEmporium Advanced 7mo ago

ML Fundamentals

Region Proposals - Explained!

CodeEmporium Advanced 7mo ago

ML Fundamentals

Eco-aware GNN Recommenders

Data Skeptic Advanced 7mo ago

ML Fundamentals

Broadridge Forecasts the Future of Asset Management with AWS | Amazon Web Services

Amazon Web Services Advanced 7mo ago

ML Fundamentals

NIMCET 2026 Preparation | NIMCET PYQ | NIMCET Free PYQ Series | NIMCET Big Announcement

GeeksforGeeks Advanced 8mo ago

ML Fundamentals

Tom Jacobs Weight Decay Controls Implicit Regularization Insights on Generalization and Sparsity

Cohere Advanced 8mo ago

ML Fundamentals

Mufan Li - Introduction to Neural Network Scaling Limits

Cohere Advanced 8mo ago

ML Fundamentals

#313 Better Predictive Models with Graph Transformers | Jure Leskovec, Professor at Stanford

DataCamp Advanced 8mo ago

ML Fundamentals

The Tech Job Market Divide: WTF is Happening?

Jean Lee Advanced 8mo ago

ML Fundamentals

Reinforcement learning with Unitree G1 humanoid - Dev w/ G1 P.5

Sentdex Advanced 8mo ago

ML Fundamentals

How Natera scales genomics with Amazon SageMaker Catalog | Amazon Web Services

Amazon Web Services Advanced 8mo ago

ML Fundamentals

Structuring a modern AI team — Denys Linkov, Wisedocs

AI Engineer Advanced 8mo ago

ML Fundamentals

Lecture 5: Sums

MIT OpenCourseWare Advanced 8mo ago

ML Fundamentals

Sponsored by: SAP | SAP and Databricks Open a Bold New Era of Data and AI​

Databricks Advanced 9mo ago

ML Fundamentals

Databricks as the Backbone of MLOps: From Orchestration to Inference

Databricks Advanced 9mo ago

ML Fundamentals

Webinar: Alumni in Action: Oxford Insights and Sustainability Leadership

Saïd Business School, University of Oxford Advanced 9mo ago

ML Fundamentals

Stanford AA222 I Engineering Design Optimization | Spring 2025 | Disciplined Convex Programming

Stanford Online Advanced 9mo ago

ML Fundamentals

The State of AI Powered Search and Retrieval — Frank Liu, MongoDB (prev Voyage AI)

AI Engineer Advanced 9mo ago

ML Fundamentals

Training models with only 4 bits | Fully-Quantized Training

Julia Turc Advanced 10mo ago

ML Fundamentals

Deep learning for DFT

Microsoft Research Advanced 10mo ago

ML Fundamentals

Grokking, Generalization Collapse, and Dynamics of Training Deep Neural Nets [Charles Martin] - 734

The TWIML AI Podcast with Sam Charrington Advanced 10mo ago

ML Fundamentals

Abstracts: March 21, 2024

Microsoft Research Advanced 10mo ago

ML Fundamentals

Tricks to Fine Tuning // Prithviraj Ammanabrolu // MLOps Podcast #318

MLOps.community Advanced 10mo ago

ML Fundamentals

Unitree G1 LiDAR, SLAM, navigation and control. Dev w/ G1 Humanoid P.2

Sentdex Advanced 11mo ago

ML Fundamentals

Image segmentation - Explained!

CodeEmporium Advanced 7mo ago

ML Fundamentals

How was object detection done before neural networks?

CodeEmporium Advanced 8mo ago

ML Fundamentals

Why neural networks are so deep? (AlexNet - Explained)

CodeEmporium Advanced 8mo ago

ML Fundamentals

Visualizing convolution networks

CodeEmporium Advanced 8mo ago

ML Fundamentals

Why convolution networks work so well (on images)

CodeEmporium Advanced 8mo ago

ML Fundamentals

Configure & use Amazon FSx for Lustre with Amazon EKS Pods | Amazon Web Services

Amazon Web Services Advanced 8mo ago

ML Fundamentals

Convolution Network back propagation by hand | the math you should know!

CodeEmporium Advanced 8mo ago

ML Fundamentals

Arthur Douillard - Distributed Training in Machine Learning

Cohere Advanced 9mo ago

ML Fundamentals

Revolutionizing Neural Network Training: Tony S.F. Explains Optimization with LMO

Cohere Advanced 9mo ago

ML Fundamentals

TAO and Reinforcement Learning: Building AI With the Data You Have

Databricks Advanced 9mo ago

ML Fundamentals

Future-Ready Data at Scale: How Shutterfly Modernized for GenAI-Driven Personalization

Databricks Advanced 9mo ago

ML Fundamentals

Tony Silveti Falls - Training neural networks at any scale

Cohere Advanced 9mo ago

ML Fundamentals

Back propagation by hand | the math you should know

CodeEmporium Advanced 9mo ago

ML Fundamentals

Andrej Jovanovic -Communication efficient training for foundation models through federated learning

Cohere Advanced 9mo ago

ML Fundamentals

How AWS machine learning supports Scuderia Ferrari HP pit stops | Amazon Web Services

Amazon Web Services Advanced 10mo ago

ML Fundamentals

Where did convolution networks come from?

CodeEmporium Advanced 10mo ago

ML Fundamentals

Primary Visual Cortex: How brain processes what we see

CodeEmporium Advanced 11mo ago

ML Fundamentals

Jeremy Bernstein - Depths of First Order Optimization

Cohere Advanced 11mo ago

ML Fundamentals

Receptive Fields - Explained

CodeEmporium Advanced 11mo ago

ML Fundamentals

A Fever Dream of Machine Learning Framework Composability

Microsoft Research Advanced 11mo ago

ML Fundamentals

Unboxing the Unitree G1 Edu Humanoid

Sentdex Advanced 11mo ago

ML Fundamentals

Visual Pathway - Explained

CodeEmporium Advanced 11mo ago

ML Fundamentals

Charles H Martin - WeightWatcher DataFree Diagnostics for Deep Learning

Cohere Advanced 1y ago

📚 Coursera Courses Opens on Coursera · Free to audit

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Developing Explainable AI (XAI)

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Health Data Science Foundation

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Computational Social Science Methods

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Introduction to Social Media Analytics

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A Crash Course in Data Science

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KALKÜLÜS I: HAZIRLIK VE ALTYAPI / CALCULUS I: PRECALCULUS

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Responsible AI: Applying AI Principles with GC - Italiano

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Introduction to AI: Key Concepts and Applications

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Data for Machine Learning

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KALKÜLÜS III: UYGULAMALAR / CALCULUS III: APPLICATIONS

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Advanced Java Concepts

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Honors Algebra 2: Algebraic, Exponential & Log Functions

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Artificial Intelligence Privacy and Convenience

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Foundations of Statistical Learning & Algorithms

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Aerial Image Segmentation with PyTorch

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Advanced Neural Network Techniques

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Calculus through Data & Modelling: Techniques of Integration

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Intermediate Data Manipulation and Machine Learning

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