Foundations

ML Fundamentals

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

11,302
lessons
Skills in this topic
View full skill map →
ML Maths Basics
beginner
Manipulate vectors and matrices
Supervised Learning
beginner
Train decision trees, random forests, and neural nets
Unsupervised Learning
intermediate
Apply k-means and DBSCAN clustering
ML Pipelines
intermediate
Engineer features and handle missing data
TensorfFlow 2 Beginner Course (3 HOURS)
ML Fundamentals
TensorfFlow 2 Beginner Course (3 HOURS)
Patrick Loeber Beginner 5y ago
DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning
ML Fundamentals
DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning
Yannic Kilcher Beginner 5y ago
What Is Probation Period?General Q&A
ML Fundamentals
What Is Probation Period?General Q&A
Krish Naik Beginner 5y ago
The Art Of Writing Resume For Data Science- Must For EveryOne
ML Fundamentals
The Art Of Writing Resume For Data Science- Must For EveryOne
Krish Naik Beginner 5y ago
L15.5 Long Short-Term Memory
ML Fundamentals
L15.5 Long Short-Term Memory
Sebastian Raschka Beginner 5y ago
L15.4 Backpropagation Through Time Overview
ML Fundamentals
L15.4 Backpropagation Through Time Overview
Sebastian Raschka Beginner 5y ago
Chris Mattmann — ML Applications on Earth, Mars, and Beyond
ML Fundamentals
Chris Mattmann — ML Applications on Earth, Mars, and Beyond
Weights & Biases Beginner 5y ago
Oxford AMLP - hybrid programme design Q&A session
ML Fundamentals ⚡ AI Lesson
Oxford AMLP - hybrid programme design Q&A session
Saïd Business School, University of Oxford Beginner 5y ago
PHD in machine learning or data science, is it worth?
ML Fundamentals
PHD in machine learning or data science, is it worth?
codebasics Beginner 5y ago
When to add more complexity in your ML infrastructure? // Daniel Stahl // MLOps Meetup Clips
ML Fundamentals
When to add more complexity in your ML infrastructure? // Daniel Stahl // MLOps Meetup Clips
MLOps.community Beginner 5y ago
How Neural Networks Can Be Hacked (And What You Should Do To Protect It)!
ML Fundamentals
How Neural Networks Can Be Hacked (And What You Should Do To Protect It)!
Patrick Loeber Beginner 5y ago
Scikit-learn Crash Course - Machine Learning Library for Python
ML Fundamentals
Scikit-learn Crash Course - Machine Learning Library for Python
freeCodeCamp.org Beginner 5y ago
PAIR AI Explorables | Is the problem in the data? Examples on Fairness, Diversity, and Bias.
ML Fundamentals
PAIR AI Explorables | Is the problem in the data? Examples on Fairness, Diversity, and Bias.
Yannic Kilcher Beginner 5y ago
ml5.js: Creative coding with ML for all - Made with TensorFlow.js
ML Fundamentals ⚡ AI Lesson
ml5.js: Creative coding with ML for all - Made with TensorFlow.js
TensorFlow Beginner 5y ago
MLOps Investments // Sarah Catanzaro // Coffee Session #33
ML Fundamentals ⚡ AI Lesson
MLOps Investments // Sarah Catanzaro // Coffee Session #33
MLOps.community Beginner 5y ago
Kmeans-based Blink Detecter DEMO
ML Fundamentals
Kmeans-based Blink Detecter DEMO
Shaw Talebi Beginner 5y ago
6. Dataloader in PyTorch
ML Fundamentals
6. Dataloader in PyTorch
Abhishek Thakur Beginner 5y ago
5. Dataset class for simple image / vision problems
ML Fundamentals
5. Dataset class for simple image / vision problems
Abhishek Thakur Beginner 5y ago
What is Word2Vec? A Simple Explanation | Deep Learning Tutorial 41 (Tensorflow, Keras & Python)
ML Fundamentals
What is Word2Vec? A Simple Explanation | Deep Learning Tutorial 41 (Tensorflow, Keras & Python)
codebasics Beginner 5y ago
ResNets are back baby!!! ...and they're gone | Machine Learning Monthly March 2021
ML Fundamentals
ResNets are back baby!!! ...and they're gone | Machine Learning Monthly March 2021
Daniel Bourke Beginner 5y ago
TensorFlow.js Community "Show & Tell" #5
ML Fundamentals ⚡ AI Lesson
TensorFlow.js Community "Show & Tell" #5
TensorFlow Beginner 5y ago
Building a Neural Network and How to Write Tests in Python | Real Python Podcast #54
ML Fundamentals
Building a Neural Network and How to Write Tests in Python | Real Python Podcast #54
Real Python Beginner 5y ago
Vladlen Koltun — The Power of Simulation and Abstraction
ML Fundamentals
Vladlen Koltun — The Power of Simulation and Abstraction
Weights & Biases Beginner 5y ago
L15.3 Different Types of Sequence Modeling Tasks
ML Fundamentals
L15.3 Different Types of Sequence Modeling Tasks
Sebastian Raschka Beginner 5y ago
L15.2 Sequence Modeling with RNNs
ML Fundamentals
L15.2 Sequence Modeling with RNNs
Sebastian Raschka Beginner 5y ago
L15.1: Different Methods for Working With Text Data
ML Fundamentals
L15.1: Different Methods for Working With Text Data
Sebastian Raschka Beginner 5y ago
L15.0: Introduction to Recurrent Neural Networks -- Lecture Overview
ML Fundamentals
L15.0: Introduction to Recurrent Neural Networks -- Lecture Overview
Sebastian Raschka Beginner 5y ago
Statistics-Left Skewed And Right Skewed Distribution And Relation With Mean, Median And Mode
ML Fundamentals
Statistics-Left Skewed And Right Skewed Distribution And Relation With Mean, Median And Mode
Krish Naik Beginner 5y ago
Stats Interview Series #1- Asked In Interview
ML Fundamentals
Stats Interview Series #1- Asked In Interview
Krish Naik Beginner 5y ago
L14.6.2 Transfer Learning in PyTorch -- Code Example
ML Fundamentals
L14.6.2 Transfer Learning in PyTorch -- Code Example
Sebastian Raschka Beginner 5y ago
L14.6.1 Transfer Learning
ML Fundamentals
L14.6.1 Transfer Learning
Sebastian Raschka Beginner 5y ago
L14.5 Convolutional Instead of Fully Connected Layers
ML Fundamentals
L14.5 Convolutional Instead of Fully Connected Layers
Sebastian Raschka Beginner 5y ago
L14.4.2 All-Convolutional Network in PyTorch -- Code Example
ML Fundamentals
L14.4.2 All-Convolutional Network in PyTorch -- Code Example
Sebastian Raschka Beginner 5y ago
L14.4.1 Replacing Max-Pooling with Convolutional Layers
ML Fundamentals
L14.4.1 Replacing Max-Pooling with Convolutional Layers
Sebastian Raschka Beginner 5y ago
Shapash- Python Library To Make Machine Learning Interpretable
ML Fundamentals
Shapash- Python Library To Make Machine Learning Interpretable
Krish Naik Beginner 5y ago
Interesting Fact About Data #shorts
ML Fundamentals
Interesting Fact About Data #shorts
Krish Naik Beginner 5y ago
4. Dataset class for simple NLP problems
ML Fundamentals
4. Dataset class for simple NLP problems
Abhishek Thakur Beginner 5y ago
3. The dataset class in PyTorch
ML Fundamentals ⚡ AI Lesson
3. The dataset class in PyTorch
Abhishek Thakur Beginner 5y ago
2. PyTorch Autograd
ML Fundamentals
2. PyTorch Autograd
Abhishek Thakur Beginner 5y ago
1. Tensors in PyTorch
ML Fundamentals
1. Tensors in PyTorch
Abhishek Thakur Beginner 5y ago
Deployment Of ML Models Using PyWebIO And Flask
ML Fundamentals
Deployment Of ML Models Using PyWebIO And Flask
Krish Naik Beginner 5y ago
What Is P Value In Statistics In Simple Language?
ML Fundamentals
What Is P Value In Statistics In Simple Language?
Krish Naik Beginner 5y ago
Creating BMI Calculator Web APP Using Python And PyWebIO
ML Fundamentals
Creating BMI Calculator Web APP Using Python And PyWebIO
Krish Naik Beginner 5y ago
PyWebIO- Creating WebAPP Using Python Without Using HTML And JS
ML Fundamentals
PyWebIO- Creating WebAPP Using Python Without Using HTML And JS
Krish Naik Beginner 5y ago
Part 4-Testing ANN Model-Audio Classification Project Using Deep Learning
ML Fundamentals
Part 4-Testing ANN Model-Audio Classification Project Using Deep Learning
Krish Naik Beginner 5y ago
L14.3.2.2 ResNet-34 in PyTorch -- Code Example
ML Fundamentals
L14.3.2.2 ResNet-34 in PyTorch -- Code Example
Sebastian Raschka Beginner 5y ago
L14.3.2.1 ResNet Overview
ML Fundamentals
L14.3.2.1 ResNet Overview
Sebastian Raschka Beginner 5y ago
L14.3.1.2 VGG16 in PyTorch -- Code Example
ML Fundamentals
L14.3.1.2 VGG16 in PyTorch -- Code Example
Sebastian Raschka Beginner 5y ago
📚 Coursera Courses Opens on Coursera · Free to audit
1 / 3 View all →
Introduction to Generative AI - Deutsch
📚 Coursera Course ↗
Self-paced
Introduction to Generative AI - Deutsch
Opens on Coursera ↗
GenAI for Employee Engagement: Driving Real-Time Insights
📚 Coursera Course ↗
Self-paced
GenAI for Employee Engagement: Driving Real-Time Insights
Opens on Coursera ↗
Optimize ML Models: Hyperparameter Tuning
📚 Coursera Course ↗
Self-paced
Optimize ML Models: Hyperparameter Tuning
Opens on Coursera ↗
Object Localization with TensorFlow
📚 Coursera Course ↗
Self-paced
Object Localization with TensorFlow
Opens on Coursera ↗
Deep Learning for Business
📚 Coursera Course ↗
Self-paced
Deep Learning for Business
Opens on Coursera ↗
Applied Fundamentals: Guess the Number
📚 Coursera Course ↗
Self-paced
Applied Fundamentals: Guess the Number
Opens on Coursera ↗