📐 ML Fundamentals

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

ML Fundamentals

Code With Me : Gibbs Sampling

ritvikmath Advanced 5y ago

ML Fundamentals

Javier Ideami on Loss Landscapes and the Flatland Perspective

Weights & Biases Beginner 5y ago

ML Fundamentals

IBM Applied AI Professional Certificate: Gain AI Skills on Coursera

Coursera Beginner 5y ago

ML Fundamentals

Explainable AI explained! | #2 By-design interpretable models with Microsofts InterpretML

DeepFindr Beginner 5y ago

ML Fundamentals

How to get started with Graph ML? (Blog walkthrough)

Aleksa Gordić - The AI Epiphany Beginner 5y ago

ML Fundamentals

L6.5 A Closer Look at the PyTorch API

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L6.4 Training ADALINE with PyTorch -- Code Example

Sebastian Raschka Beginner 5y ago

ML Fundamentals

Session On Different Types Of Loss Function In Deep Learning

Krish Naik Intermediate 5y ago

ML Fundamentals

Coding MCMC : Data Science Code

ritvikmath Intermediate 5y ago

ML Fundamentals

How to solve Santander Kaggle Transaction Competition [Top 1% Solution, No Ensemble]

Aladdin Persson Beginner 5y ago

ML Fundamentals

Sunday Late Night Live Q&A - Ask Anything Related To Data Science

Krish Naik Intermediate 5y ago

ML Fundamentals

An AI software able to detect and count plastic waste in the ocean

What's AI by Louis-François Bouchard Beginner 5y ago

ML Fundamentals

Piero Molino — The Secret Behind Building Successful Open Source Projects

Weights & Biases Beginner 5y ago

ML Fundamentals

The Art of Learning Data Science (How to learn data science)

Data Professor Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

How to do the Titanic Kaggle Competition

Aladdin Persson Beginner 5y ago

ML Fundamentals

Intel: Machine Learning and the Future of the Data Center w/Intel

The New Stack Beginner 5y ago

ML Fundamentals

Speech Command Recognition With Tensorflow.JS and React.JS | Javascript AI

Nicholas Renotte Intermediate 5y ago

ML Fundamentals ⚡ AI Lesson

What is OneAPI? The Software Tool Gap: A Roundtable Discussion

The New Stack Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

Join us at TensorFlow Everywhere

TensorFlow Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

How does a Data Scientist Fight FRAUD?

CodeEmporium Beginner 5y ago

ML Fundamentals

Push Notifications from Jupyter Notebook after Code Execution [Python for Data Science]

1littlecoder Beginner 5y ago

ML Fundamentals

The SoftMax Derivative, Step-by-Step!!!

StatQuest with Josh Starmer Beginner 5y ago

ML Fundamentals

Neural Networks Part 5: ArgMax and SoftMax

StatQuest with Josh Starmer Beginner 5y ago

ML Fundamentals

Building a recommendation system using deep learning

Abhishek Thakur Intermediate 5y ago

ML Fundamentals

L6.3 Automatic Differentiation in PyTorch -- Code Example

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L6.2 Understanding Automatic Differentiation via Computation Graphs

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L6.1 Learning More About PyTorch

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L6.0 Automatic Differentiation in PyTorch -- Lecture Overview

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L5.8 Adaline Code Example

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L5.7 Training an Adaptive Linear Neuron (Adaline)

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L5.6 Understanding Gradient Descent

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L5.5 (Optional) Calculus Refresher II: Gradients

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L5.4 (Optional) Calculus Refresher I: Derivatives

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L5.3 An Iterative Training Algorithm for Linear Regression

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L5.2 Relation Between Perceptron and Linear Regression

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L5.1 Online, Batch, and Minibatch Mode

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L5.0 Gradient Descent -- Lecture Overview

Sebastian Raschka Beginner 5y ago

ML Fundamentals

Democratize AI with OneAPI

The New Stack Intermediate 5y ago

ML Fundamentals ⚡ AI Lesson

Intel: How Google Health Uses Machine Learning With Intel

The New Stack Beginner 5y ago

ML Fundamentals

Bayesian Treasure Hunt : Data Science Code

ritvikmath Intermediate 5y ago

ML Fundamentals

L4.5 A Fully Connected (Linear) Layer in PyTorch

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L4.4 Notational Conventions for Neural Networks

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L4.3 Vectors, Matrices, and Broadcasting

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L4.2 Tensors in PyTorch

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L4.1 Tensors in Deep Learning

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L4.0 Linear Algebra for Deep Learning -- Lecture Overview

Sebastian Raschka Beginner 5y ago

ML Fundamentals

Advice on Publishing Machine Learning Papers with MLC's founder Rosanne Liu

Weights & Biases Advanced 5y ago

ML Fundamentals

What the Heck is Bayesian Stats ?? : Data Science Basics

ritvikmath Beginner 5y ago

📚 Coursera Courses Opens on Coursera · Free to audit

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Self-paced

Recommender Systems: An Applied Approach using Deep Learning

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Optimizing AI Workflows and Deploying Edge Models

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📚 Coursera Course ↗

Self-paced

Encoder-Decoder Architecture - Italiano

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📚 Coursera Course ↗

Self-paced

Advanced CNNs, Transfer Learning, and Recurrent Networks

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Self-paced

Applied Fundamentals: Guess the Number

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Power System Stability

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Interpretable Machine Learning Applications: Part 4

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Self-paced

Machine Learning: Random Forest with Python from Scratch©

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📚 Coursera Course ↗

Self-paced

Algebra: Elementary to Advanced - Equations & Inequalities

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Semantic Segmentation with Amazon Sagemaker

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📚 Coursera Course ↗

Self-paced

Logistic Regression with R: Build & Predict

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📚 Coursera Course ↗

Self-paced

R Programming: Data Analysis and Modeling

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📚 Coursera Course ↗

Self-paced

Foundations of Data Structures & Algorithms in Python

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📚 Coursera Course ↗

Self-paced

Trabalhar com Notebooks na Vertex AI

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📚 Coursera Course ↗

Self-paced

Orchestrate ML Workflows with Vertex AI Pipelines

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📚 Coursera Course ↗

Self-paced

Transfer Learning for NLP with TensorFlow Hub

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📚 Coursera Course ↗

Self-paced

Machine Learning for Engineers: Algorithms and Applications

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📚 Coursera Course ↗

Self-paced

Recommender Systems

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