Foundations

ML Fundamentals

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

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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
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Who Are Data Scientists?
ML Fundamentals
Who Are Data Scientists?
Krish Naik Intermediate 4y ago
Building a Data Center Inside Your Laptop - Computerphile
ML Fundamentals
Building a Data Center Inside Your Laptop - Computerphile
Computerphile Intermediate 4y ago
Luigi in Production Part 2 // Luigi Patruno // MLOps Coffee Sessions #36
ML Fundamentals
Luigi in Production Part 2 // Luigi Patruno // MLOps Coffee Sessions #36
MLOps.community Beginner 4y ago
Deep Learning Interview Series #2- Asked In interview
ML Fundamentals
Deep Learning Interview Series #2- Asked In interview
Krish Naik Beginner 4y ago
Train and Debug YOLOv5 Models with Weights & Biases Integration | YOLOv5 Series Part 0
ML Fundamentals
Train and Debug YOLOv5 Models with Weights & Biases Integration | YOLOv5 Series Part 0
Weights & Biases Beginner 4y ago
L18.4: A GAN for Generating Handwritten Digits in PyTorch -- Code Example
ML Fundamentals
L18.4: A GAN for Generating Handwritten Digits in PyTorch -- Code Example
Sebastian Raschka Beginner 4y ago
L18.3: Modifying the GAN Loss Function for Practical Use
ML Fundamentals
L18.3: Modifying the GAN Loss Function for Practical Use
Sebastian Raschka Beginner 4y ago
Natural Language Processing (NLP) and Text Classification With Python
ML Fundamentals
Natural Language Processing (NLP) and Text Classification With Python
Real Python Beginner 4y ago
Adrien Gaidon — Advancing ML Research in Autonomous Vehicles
ML Fundamentals
Adrien Gaidon — Advancing ML Research in Autonomous Vehicles
Weights & Biases Beginner 4y ago
Banking for Climate Change Mitigation: Opportunities and Challenges
ML Fundamentals
Banking for Climate Change Mitigation: Opportunities and Challenges
Saïd Business School, University of Oxford Beginner 4y ago
Advanced Developer Workloads with Built-In AI Acceleration
ML Fundamentals
Advanced Developer Workloads with Built-In AI Acceleration
The New Stack Advanced 4y ago
Backpropagation : Data Science Concepts
ML Fundamentals
Backpropagation : Data Science Concepts
ritvikmath Beginner 4y ago
MLOps Community 1 Year Anniversary! // Meetup #59 clip
ML Fundamentals
MLOps Community 1 Year Anniversary! // Meetup #59 clip
MLOps.community Beginner 4y ago
Node-Red: Visual coding for ML on Raspberry Pi and beyond - Made with TensorFlow.js
ML Fundamentals
Node-Red: Visual coding for ML on Raspberry Pi and beyond - Made with TensorFlow.js
TensorFlow Beginner 4y ago
Intro to Neural Networks : Data Science Concepts
ML Fundamentals
Intro to Neural Networks : Data Science Concepts
ritvikmath Beginner 4y ago
1. Live coding Jarvis Transcriptions for Speech to Text Dataset p.1
ML Fundamentals
1. Live coding Jarvis Transcriptions for Speech to Text Dataset p.1
sentdex Beginner 4y ago
Preprocessing data for Machine Learning - Deep Dive
ML Fundamentals
Preprocessing data for Machine Learning - Deep Dive
CodeEmporium Beginner 4y ago
Flesch Kincaid Readability Tests
ML Fundamentals
Flesch Kincaid Readability Tests
Data Skeptic Beginner 4y ago
Multiclass logistic/softmax regression from scratch
ML Fundamentals
Multiclass logistic/softmax regression from scratch
Sophia Yang Intermediate 4y ago
Introduction | Mathematics and statistics for data science and machine learning
ML Fundamentals
Introduction | Mathematics and statistics for data science and machine learning
codebasics Beginner 4y ago
What is logarithm? | Math, Statistics for data science, machine learning
ML Fundamentals
What is logarithm? | Math, Statistics for data science, machine learning
codebasics Beginner 4y ago
Building Models with PyTorch
ML Fundamentals
Building Models with PyTorch
PyTorch Beginner 4y ago
Conversational AI w/ Jarvis - checking out the API
ML Fundamentals
Conversational AI w/ Jarvis - checking out the API
sentdex Beginner 4y ago
Human-Centered ML for High-Risk Behaviors with Stevie Chancellor - #472
ML Fundamentals
Human-Centered ML for High-Risk Behaviors with Stevie Chancellor - #472
The TWIML AI Podcast with Sam Charrington Beginner 4y ago
L18.2: The GAN Objective
ML Fundamentals
L18.2: The GAN Objective
Sebastian Raschka Beginner 4y ago
L18.1: The Main Idea Behind GANs
ML Fundamentals
L18.1: The Main Idea Behind GANs
Sebastian Raschka Beginner 4y ago
L18.0: Introduction to Generative Adversarial Networks -- Lecture Overview
ML Fundamentals
L18.0: Introduction to Generative Adversarial Networks -- Lecture Overview
Sebastian Raschka Beginner 4y ago
Automating Web Scrapping Using AutoScraper Library
ML Fundamentals
Automating Web Scrapping Using AutoScraper Library
Krish Naik Intermediate 4y ago
L17.7 VAE Latent Space Arithmetic in PyTorch -- Making People Smile (Code Example)
ML Fundamentals
L17.7 VAE Latent Space Arithmetic in PyTorch -- Making People Smile (Code Example)
Sebastian Raschka Beginner 4y ago
L17.6 A Variational Autoencoder for Face Images in PyTorch -- Code Example
ML Fundamentals
L17.6 A Variational Autoencoder for Face Images in PyTorch -- Code Example
Sebastian Raschka Beginner 4y ago
L17.5 A Variational Autoencoder for Handwritten Digits in PyTorch -- Code Example
ML Fundamentals
L17.5 A Variational Autoencoder for Handwritten Digits in PyTorch -- Code Example
Sebastian Raschka Beginner 4y ago
L17.4 Variational Autoencoder Loss Function
ML Fundamentals
L17.4 Variational Autoencoder Loss Function
Sebastian Raschka Beginner 4y ago
L17.3 The Log-Var Trick
ML Fundamentals
L17.3 The Log-Var Trick
Sebastian Raschka Beginner 4y ago
L17.2 Sampling from a Variational Autoencoder
ML Fundamentals
L17.2 Sampling from a Variational Autoencoder
Sebastian Raschka Beginner 4y ago
L17.1 Variational Autoencoder Overview
ML Fundamentals
L17.1 Variational Autoencoder Overview
Sebastian Raschka Beginner 4y ago
L17.0 Intro to Variational Autoencoders -- Lecture Overview
ML Fundamentals
L17.0 Intro to Variational Autoencoders -- Lecture Overview
Sebastian Raschka Beginner 4y ago
Machine Learning Interview Series#1-Asked in Interview
ML Fundamentals
Machine Learning Interview Series#1-Asked in Interview
Krish Naik Beginner 4y ago
War Stories Productionising ML // Nick Masca // Coffee Session#35
ML Fundamentals
War Stories Productionising ML // Nick Masca // Coffee Session#35
MLOps.community Beginner 4y ago
Introduction To Machine Learning And Deep Learning For Starters
ML Fundamentals
Introduction To Machine Learning And Deep Learning For Starters
Krish Naik Beginner 4y ago
Deep Learning Interview Series #1- Asked In Interview
ML Fundamentals
Deep Learning Interview Series #1- Asked In Interview
Krish Naik Beginner 4y ago
Friday Live Q&A Ask Anything Related Data Science
ML Fundamentals
Friday Live Q&A Ask Anything Related Data Science
Krish Naik Beginner 4y ago
L16.5 Other Types of Autoencoders
ML Fundamentals
L16.5 Other Types of Autoencoders
Sebastian Raschka Beginner 4y ago
L16.4 A Convolutional Autoencoder in PyTorch -- Code Example
ML Fundamentals
L16.4 A Convolutional Autoencoder in PyTorch -- Code Example
Sebastian Raschka Beginner 4y ago
L16.3 Convolutional Autoencoders & Transposed Convolutions
ML Fundamentals
L16.3 Convolutional Autoencoders & Transposed Convolutions
Sebastian Raschka Beginner 4y ago
L16.2 A Fully-Connected Autoencoder
ML Fundamentals
L16.2 A Fully-Connected Autoencoder
Sebastian Raschka Beginner 4y ago
L16.1 Dimensionality Reduction
ML Fundamentals
L16.1 Dimensionality Reduction
Sebastian Raschka Beginner 4y ago
L16.0 Introduction to Autoencoders -- Lecture Overview
ML Fundamentals
L16.0 Introduction to Autoencoders -- Lecture Overview
Sebastian Raschka Beginner 4y ago
Nimrod Shabtay — Deployment and Monitoring at Nanit
ML Fundamentals
Nimrod Shabtay — Deployment and Monitoring at Nanit
Weights & Biases Beginner 4y ago
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