📐 ML Fundamentals

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

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L5.2 Relation Between Perceptron and Linear Regression

ML Fundamentals

L5.2 Relation Between Perceptron and Linear Regression

Sebastian Raschka Beginner 5y ago

L5.1 Online, Batch, and Minibatch Mode

ML Fundamentals

L5.1 Online, Batch, and Minibatch Mode

Sebastian Raschka Beginner 5y ago

Piero Molino — The Secret Behind Building Successful Open Source Projects

ML Fundamentals

Piero Molino — The Secret Behind Building Successful Open Source Projects

Weights & Biases Beginner 5y ago

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

ML Fundamentals

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

Data Professor Beginner 5y ago

Navigating the next normal: A view from female leaders

ML Fundamentals

Navigating the next normal: A view from female leaders

Saïd Business School, University of Oxford Intermediate 5y ago

How to do the Titanic Kaggle Competition

ML Fundamentals

How to do the Titanic Kaggle Competition

Aladdin Persson Beginner 5y ago

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

ML Fundamentals

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

The New Stack Beginner 5y ago

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

ML Fundamentals

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

Nicholas Renotte Intermediate 5y ago

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

ML Fundamentals

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

The New Stack Beginner 5y ago

Join us at TensorFlow Everywhere

ML Fundamentals

Join us at TensorFlow Everywhere

TensorFlow Beginner 5y ago

Bayesian Treasure Hunt : Data Science Code

ML Fundamentals

Bayesian Treasure Hunt : Data Science Code

ritvikmath Intermediate 5y ago

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

ML Fundamentals

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

Weights & Biases Advanced 5y ago

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

ML Fundamentals

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

ritvikmath Beginner 5y ago

How does a Data Scientist Fight FRAUD?

ML Fundamentals

How does a Data Scientist Fight FRAUD?

CodeEmporium Beginner 5y ago

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

ML Fundamentals

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

1littlecoder Beginner 5y ago

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

ML Fundamentals

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

StatQuest with Josh Starmer Beginner 5y ago

Neural Networks Part 5: ArgMax and SoftMax

ML Fundamentals

Neural Networks Part 5: ArgMax and SoftMax

StatQuest with Josh Starmer Beginner 5y ago

Building a recommendation system using deep learning

ML Fundamentals

Building a recommendation system using deep learning

Abhishek Thakur Intermediate 5y ago

Build a 2D convolutional neural network, part 17: Cottonwood cheatsheet

ML Fundamentals

Build a 2D convolutional neural network, part 17: Cottonwood cheatsheet

Brandon Rohrer Intermediate 5y ago

Build a 2D convolutional neural network, part 16: Cottonwood code tour

ML Fundamentals

Build a 2D convolutional neural network, part 16: Cottonwood code tour

Brandon Rohrer Intermediate 5y ago

Simple Explanation of LSTM | Deep Learning Tutorial 36 (Tensorflow, Keras & Python)

ML Fundamentals

Simple Explanation of LSTM | Deep Learning Tutorial 36 (Tensorflow, Keras & Python)

codebasics Beginner 5y ago

L5.0 Gradient Descent -- Lecture Overview

ML Fundamentals

L5.0 Gradient Descent -- Lecture Overview

Sebastian Raschka Beginner 5y ago

Democratize AI with OneAPI

ML Fundamentals

Democratize AI with OneAPI

The New Stack Intermediate 5y ago

Intel: How Google Health Uses Machine Learning With Intel

ML Fundamentals

Intel: How Google Health Uses Machine Learning With Intel

The New Stack Beginner 5y ago

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

ML Fundamentals

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

Sebastian Raschka Beginner 5y ago

L4.4 Notational Conventions for Neural Networks

ML Fundamentals

L4.4 Notational Conventions for Neural Networks

Sebastian Raschka Beginner 5y ago

L4.3 Vectors, Matrices, and Broadcasting

ML Fundamentals

L4.3 Vectors, Matrices, and Broadcasting

Sebastian Raschka Beginner 5y ago

L4.2 Tensors in PyTorch

ML Fundamentals

L4.2 Tensors in PyTorch

Sebastian Raschka Beginner 5y ago

L4.1 Tensors in Deep Learning

ML Fundamentals

L4.1 Tensors in Deep Learning

Sebastian Raschka Beginner 5y ago

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

ML Fundamentals

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

Sebastian Raschka Beginner 5y ago

Build a 2D convolutional neural network, part 15: Rendering examples

ML Fundamentals

Build a 2D convolutional neural network, part 15: Rendering examples

Brandon Rohrer Intermediate 5y ago

Build a 2D convolutional neural network, part 14: Collecting examples

ML Fundamentals

Build a 2D convolutional neural network, part 14: Collecting examples

Brandon Rohrer Intermediate 5y ago

Build a 2D convolutional neural network, part 13: Loss history and text summary

ML Fundamentals

Build a 2D convolutional neural network, part 13: Loss history and text summary

Brandon Rohrer Intermediate 5y ago

Build a 2D convolutional neural network, part 12: Testing loop

ML Fundamentals

Build a 2D convolutional neural network, part 12: Testing loop

Brandon Rohrer Intermediate 5y ago

Build a 2D convolutional neural network, part 11: The training loop

ML Fundamentals

Build a 2D convolutional neural network, part 11: The training loop

Brandon Rohrer Intermediate 5y ago

Build a 2D convolutional neural network, part 10: Connecting layers

ML Fundamentals

Build a 2D convolutional neural network, part 10: Connecting layers

Brandon Rohrer Intermediate 5y ago

Build a 2D convolutional neural network, part 9: Adding layers

ML Fundamentals

Build a 2D convolutional neural network, part 9: Adding layers

Brandon Rohrer Intermediate 5y ago

Build a 2D convolutional neural network, part 8: Training code setup

ML Fundamentals

Build a 2D convolutional neural network, part 8: Training code setup

Brandon Rohrer Intermediate 5y ago

Build a 2D convolutional neural network, part 7: Why Cottonwood?

ML Fundamentals

Build a 2D convolutional neural network, part 7: Why Cottonwood?

Brandon Rohrer Intermediate 5y ago

Build a 2D convolutional neural network, part 6: Examples of successes and failures

ML Fundamentals

Build a 2D convolutional neural network, part 6: Examples of successes and failures

Brandon Rohrer Intermediate 5y ago

Build a 2D convolutional neural network, part 5: Pre-trained model results

ML Fundamentals

Build a 2D convolutional neural network, part 5: Pre-trained model results

Brandon Rohrer Intermediate 5y ago

Build a 2D convolutional neural network, part 4: Model overview

ML Fundamentals

Build a 2D convolutional neural network, part 4: Model overview

Brandon Rohrer Intermediate 5y ago

Build a 2D convolutional neural network, part 3: MNIST digits

ML Fundamentals

Build a 2D convolutional neural network, part 3: MNIST digits

Brandon Rohrer Intermediate 5y ago

Build a 2D convolutional neural network, part 2: Overview

ML Fundamentals

Build a 2D convolutional neural network, part 2: Overview

Brandon Rohrer Intermediate 5y ago

Build a 2D convolutional neural network, part 1: Getting started

ML Fundamentals

Build a 2D convolutional neural network, part 1: Getting started

Brandon Rohrer Intermediate 5y ago

Build a 1D convolutional neural network, part 7: Evaluate the model

ML Fundamentals

Build a 1D convolutional neural network, part 7: Evaluate the model

Brandon Rohrer Beginner 5y ago

Build a 1D convolutional neural network, part 6: Text summary and loss history

ML Fundamentals

Build a 1D convolutional neural network, part 6: Text summary and loss history

Brandon Rohrer Beginner 5y ago

Build a 1D convolutional neural network, part 5: One Hot, Flatten, and Logging blocks

ML Fundamentals

Build a 1D convolutional neural network, part 5: One Hot, Flatten, and Logging blocks

Brandon Rohrer Beginner 5y ago