Foundations

ML Fundamentals

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

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Manipulate vectors and matrices
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beginner
Train decision trees, random forests, and neural nets
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intermediate
Apply k-means and DBSCAN clustering
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Engineer features and handle missing data
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L6.0 Automatic Differentiation in PyTorch -- Lecture Overview
ML Fundamentals
L6.0 Automatic Differentiation in PyTorch -- Lecture Overview
Sebastian Raschka Beginner 5y ago
Session On Different Types Of Loss Function In Deep Learning
ML Fundamentals
Session On Different Types Of Loss Function In Deep Learning
Krish Naik Intermediate 5y ago
Coding MCMC : Data Science Code
ML Fundamentals
Coding MCMC : Data Science Code
ritvikmath Intermediate 5y ago
How to solve Santander Kaggle Transaction Competition [Top 1% Solution, No Ensemble]
ML Fundamentals
How to solve Santander Kaggle Transaction Competition [Top 1% Solution, No Ensemble]
Aladdin Persson Beginner 5y ago
Sunday Late Night Live Q&A - Ask Anything Related To Data Science
ML Fundamentals
Sunday Late Night Live Q&A - Ask Anything Related To Data Science
Krish Naik Intermediate 5y ago
An AI software able to detect and count plastic waste in the ocean
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
Ubicomp (Ubiquitous Computing) - Computerphile
ML Fundamentals
Ubicomp (Ubiquitous Computing) - Computerphile
Computerphile Intermediate 5y ago
L5.8 Adaline Code Example
ML Fundamentals
L5.8 Adaline Code Example
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
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
L5.7 Training an Adaptive Linear Neuron (Adaline)
ML Fundamentals
L5.7 Training an Adaptive Linear Neuron (Adaline)
Sebastian Raschka Beginner 5y ago
L5.6 Understanding Gradient Descent
ML Fundamentals
L5.6 Understanding Gradient Descent
Sebastian Raschka Beginner 5y ago
L5.5 (Optional) Calculus Refresher II: Gradients
ML Fundamentals
L5.5 (Optional) Calculus Refresher II: Gradients
Sebastian Raschka Beginner 5y ago
L5.4 (Optional) Calculus Refresher I: Derivatives
ML Fundamentals
L5.4 (Optional) Calculus Refresher I: Derivatives
Sebastian Raschka Beginner 5y ago
L5.3 An Iterative Training Algorithm for Linear Regression
ML Fundamentals
L5.3 An Iterative Training Algorithm for Linear Regression
Sebastian Raschka Beginner 5y ago
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
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
What the Heck is Bayesian Stats ?? : Data Science Basics
ML Fundamentals
What the Heck is Bayesian Stats ?? : Data Science Basics
ritvikmath 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
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