📐 ML Fundamentals

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

ML Fundamentals

L16.4 A Convolutional Autoencoder in PyTorch -- Code Example

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L16.3 Convolutional Autoencoders & Transposed Convolutions

Sebastian Raschka Beginner 5y ago

ML Fundamentals

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

Predicting Crypto Prices in Python

NeuralNine Beginner 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

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

Two Minute Papers Beginner 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

Training with PyTorch

PyTorch Beginner 5y ago

ML Fundamentals

7. Linear regression model in PyTorch

Abhishek Thakur Beginner 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

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

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

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

ml5.js: Creative coding with ML for all - Made with TensorFlow.js

TensorFlow Beginner 5y ago

ML Fundamentals

MLOps Investments // Sarah Catanzaro // Coffee Session #33

MLOps.community Beginner 5y ago

ML Fundamentals

L16.2 A Fully-Connected Autoencoder

Sebastian Raschka Beginner 5y ago

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

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

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

PAIR AI Explorables | Is the problem in the data? Examples on Fairness, Diversity, and Bias.

Yannic Kilcher Beginner 5y ago

ML Fundamentals

L14.6.2 Transfer Learning in PyTorch -- Code Example

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L14.6.1 Transfer Learning

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L14.5 Convolutional Instead of Fully Connected Layers

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L14.4.2 All-Convolutional Network in PyTorch -- Code Example

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L14.4.1 Replacing Max-Pooling with Convolutional Layers

Sebastian Raschka Beginner 5y ago

ML Fundamentals

Shapash- Python Library To Make Machine Learning Interpretable

Krish Naik Beginner 5y ago

📚 Coursera Courses Opens on Coursera · Free to audit

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Introduction to Machine Learning: Unsupervised Learning

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Fundamentals of Machine Learning in Finance

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Build Intelligent iOS Apps with Core ML 3: Learn & Apply AI

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📚 Coursera Course ↗

Self-paced

Advanced Recommender Systems

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📚 Coursera Course ↗

Self-paced

Physics 102 - Electric Charges and Fields

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📚 Coursera Course ↗

Self-paced

Física: Vectores, Trabajo y Energía

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Trabalhar com Notebooks na Vertex AI

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From Zero to Hero - Digital Product Development From Scratch

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📚 Coursera Course ↗

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GenAI and Model Selection

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📚 Coursera Course ↗

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TradeStation EasyLanguage for Algorithmic Trading

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📚 Coursera Course ↗

Self-paced

Databricks Associate Developer: Apache Spark with Python

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📚 Coursera Course ↗

Self-paced

Mathematics for Machine Learning: Linear Algebra

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📚 Coursera Course ↗

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Machine Learning: Concepts and Applications

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📚 Coursera Course ↗

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Unsupervised Machine Learning

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📚 Coursera Course ↗

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Geometric Algorithms

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Data Visualization and Modeling in Python

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Advanced Methods in Machine Learning Applications

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📚 Coursera Course ↗

Self-paced

Calculus through Data & Modeling: Applying Differentiation

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