📐 ML Fundamentals

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

ML Fundamentals

L16.1 Dimensionality Reduction

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L16.0 Introduction to Autoencoders -- Lecture Overview

Sebastian Raschka Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

Human-Centered ML for High-Risk Behaviors with Stevie Chancellor - #472

The TWIML AI Podcast with Sam Charrington Beginner 5y ago

ML Fundamentals

Nimrod Shabtay — Deployment and Monitoring at Nanit

Weights & Biases Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

Predicting Crypto Prices in Python

NeuralNine Beginner 5y ago

ML Fundamentals

The Viterbi Algorithm : Natural Language Processing

ritvikmath Intermediate 5y ago

ML Fundamentals

I BUILT A NEURAL NETWORK IN MINECRAFT | Analog Redstone Network w/ Backprop & Optimizer (NO MODS)

Yannic Kilcher Beginner 5y ago

ML Fundamentals

Learner Spotlight: My Journey into AI

DeepLearningAI Intermediate 5y ago

ML Fundamentals

Do Neural Networks Think Like Our Brain? OpenAI Answers! 🧠

Two Minute Papers Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

adversarial.js: Break any neural network live in your browser - Made with TensorFlow.js

TensorFlow Intermediate 5y ago

ML Fundamentals

Mars rovers and machine learning with NASA JPL's Chris Mattmann

Weights & Biases Beginner 5y ago

ML Fundamentals

Production Inference Deployment with PyTorch

PyTorch Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

Training with PyTorch

PyTorch Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

7. Linear regression model in PyTorch

Abhishek Thakur Beginner 5y ago

ML Fundamentals

Accelerating Drug Discovery with Clara Discovery's MegaMolBart

NVIDIA Developer Intermediate 5y ago

ML Fundamentals

Self-Tuning Networks: Amortizing the Hypergradient Computation for Hyperparameter Optimization

Microsoft Research Advanced 5y ago

ML Fundamentals

Part of Speech Tagging : Natural Language Processing

ritvikmath Beginner 5y ago

ML Fundamentals

Difference Between fit(), transform(), fit_transform() and predict() methods in Scikit-Learn

Krish Naik Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

I Built An AI That Destroys Watermarks

Aladdin Persson Beginner 5y ago

ML Fundamentals

CLIP: El Ojo MÁS POTENTE de la INTELIGENCIA ARTIFICIAL!

Dot CSV Beginner 5y ago

ML Fundamentals

Model & Data Drift Deep Dive // Ben Wilson // MLOps Meetup Clips

MLOps.community Intermediate 5y ago

ML Fundamentals

TensorfFlow 2 Beginner Course (3 HOURS)

Patrick Loeber Beginner 5y ago

ML Fundamentals

DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning

Yannic Kilcher Beginner 5y ago

ML Fundamentals

What Is Probation Period?General Q&A

Krish Naik Beginner 5y ago

ML Fundamentals

Tensorflow Object Detection in 5 Hours with Python | Full Course with 3 Projects

Nicholas Renotte Intermediate 5y ago

ML Fundamentals ⚡ AI Lesson

Oxford AMLP - hybrid programme design Q&A session

Saïd Business School, University of Oxford Beginner 5y ago

ML Fundamentals

PHD in machine learning or data science, is it worth?

codebasics Beginner 5y ago

ML Fundamentals

When to add more complexity in your ML infrastructure? // Daniel Stahl // MLOps Meetup Clips

MLOps.community Beginner 5y ago

ML Fundamentals

How Neural Networks Can Be Hacked (And What You Should Do To Protect It)!

Patrick Loeber Beginner 5y ago

ML Fundamentals

Scikit-learn Crash Course - Machine Learning Library for Python

freeCodeCamp.org Beginner 5y ago

ML Fundamentals

L15.7 An RNN Sentiment Classifier in PyTorch

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L15.6 RNNs for Classification: A Many-to-One Word RNN

Sebastian Raschka Beginner 5y ago

ML Fundamentals

PyTorch TensorBoard Support

PyTorch Beginner 5y ago

ML Fundamentals

The Art Of Writing Resume For Data Science- Must For EveryOne

Krish Naik Beginner 5y ago

ML Fundamentals

Amazing Initiative For Kids- Ineuron K-12 Youtube Channel- Free Content #shorts

Krish Naik Intermediate 5y ago

ML Fundamentals

How To Handle 90 Days Notice Period With HR

Krish Naik Intermediate 5y ago

ML Fundamentals

Stats Interview Series #2-Asked In Interview

Krish Naik Intermediate 5y ago

ML Fundamentals

L15.5 Long Short-Term Memory

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L15.4 Backpropagation Through Time Overview

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L15.3 Different Types of Sequence Modeling Tasks

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L15.2 Sequence Modeling with RNNs

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L15.1: Different Methods for Working With Text Data

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L15.0: Introduction to Recurrent Neural Networks -- Lecture Overview

Sebastian Raschka Beginner 5y ago

ML Fundamentals

Chris Mattmann — ML Applications on Earth, Mars, and Beyond

Weights & Biases Beginner 5y ago

ML Fundamentals

Statistics-Left Skewed And Right Skewed Distribution And Relation With Mean, Median And Mode

Krish Naik Beginner 5y ago

ML Fundamentals

Stats Interview Series #1- Asked In Interview

Krish Naik Beginner 5y ago

ML Fundamentals

Start-ups vs Enterprise OSS Strategy in Machine Learning Tooling // MLOps Clips with Sarah Catanzaro

MLOps.community Intermediate 5y ago

ML Fundamentals

Interview Question -Risks In Data Science Projects

Krish Naik Intermediate 5y ago

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Amazon Kinesis Video Streams - Getting Started

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Custom Models, Layers, and Loss Functions with TensorFlow

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Self-paced

Building, Evaluating, and Operationalizing ML Models

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📚 Coursera Course ↗

Self-paced

Create Image Captioning Models - Deutsch

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Developing Machine Learning Solutions

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Prepare for DP-100: Data Science on Microsoft Azure Exam

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PyTorch: Techniques and Ecosystem Tools

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Análisis de documentos con servicios cognitivos de Azure

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Supervised Machine Learning: Classification

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H2O Gen AI Ecosystem Overview - Level 2

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Machine Learning with Small Data Part 2

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Deep Learning with TensorFlow: Build Neural Networks

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📚 Coursera Course ↗

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Credit Default Prediction with Python: Apply & Analyze

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Pricing Options with Mathematical Models

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📚 Coursera Course ↗

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Matrix Factorization and Advanced Techniques

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Python: Implement & Evaluate Random Forests for ML

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Mastering TQM: Apply, Evaluate & Improve Quality Systems

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Microsoft Azure - Basics

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