📐 ML Fundamentals

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

ML Fundamentals

Implement 1D convolution, part 7: Weight gradient and input gradient

Brandon Rohrer Beginner 5y ago

ML Fundamentals

Implement 1D convolution, part 6: Multi-channel, multi-kernel convolutions

Brandon Rohrer Beginner 5y ago

ML Fundamentals

Run Jupyter Lab for Python, R, Swift from Google Colab with ColabCode

1littlecoder Beginner 5y ago

ML Fundamentals

L3.1 About Brains and Neurons

Sebastian Raschka Beginner 5y ago

ML Fundamentals

Rosanne Liu — Conducting Fundamental ML Research as a Nonprofit

Weights & Biases Beginner 5y ago

ML Fundamentals

L3.5 The Geometric Intuition Behind the Perceptron

Sebastian Raschka Beginner 5y ago

ML Fundamentals

Deep Networks Are Kernel Machines (Paper Explained)

Yannic Kilcher Beginner 5y ago

ML Fundamentals

Let's talk about AGI: Elon Musk vs Andrew Ng on Superintelligence

Aladdin Persson Beginner 5y ago

ML Fundamentals

Capturing Object Detection History with Tensorflow Object Detection and Python

Nicholas Renotte Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

Code With Me : Decision Trees

ritvikmath Advanced 5y ago

ML Fundamentals

This Neural Network Makes Virtual Humans Dance! 🕺

Two Minute Papers Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

Predicting Stock Prices in Python

NeuralNine Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

A Future of Work for the Invisible Workers in A.I. with Saiph Savage - #447

The TWIML AI Podcast with Sam Charrington Beginner 5y ago

ML Fundamentals

Object Localization Vs Object Detection Deep Learning

Krish Naik Intermediate 5y ago

ML Fundamentals

The Importance and Concern of NLP in National Intelligence with Sean Gourley, Primer CEO

Weights & Biases Beginner 5y ago

ML Fundamentals

15 Programming Project Ideas - From Beginner to Advanced

Tech With Tim Beginner 5y ago

ML Fundamentals

How To Implement Image Classification Using SVM In Convolution Neural Network

Krish Naik Intermediate 5y ago

ML Fundamentals ⚡ AI Lesson

PyTorch Quick Tip: Mixed Precision Training (FP16)

Aladdin Persson Beginner 5y ago

ML Fundamentals

Career Advice Office Hours - Job Applications | Coursera

Coursera Intermediate 5y ago

ML Fundamentals

Practical MLOps // Noah Gift // MLOps Coffee Sessions #27

MLOps.community Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

Automated Videoing Assistant - Made with TensorFlow.js

TensorFlow Beginner 5y ago

ML Fundamentals

2021 Microsoft Research Ada Lovelace Fellow: Demba Komma

Microsoft Research Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

Implement 1D convolution, part 5: Forward and backward pass

Brandon Rohrer Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

Implement 1D convolution, part 4: Initialize the convolution block

Brandon Rohrer Beginner 5y ago

ML Fundamentals

Implement 1D convolution, part 3: Create the convolution block

Brandon Rohrer Beginner 5y ago

ML Fundamentals

Implement 1D convolution, part 2: Comparison with NumPy convolution()

Brandon Rohrer Beginner 5y ago

ML Fundamentals

Implement 1D convolution, part 1: Convolution in Python from scratch

Brandon Rohrer Advanced 5y ago

ML Fundamentals

L3.3 Vectorization in Python

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L3.4 Perceptron in Python using NumPy and PyTorch

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L3.2 The Perceptron Learning Rule

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L3.0 Perceptron Lecture Overview

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L2.4 The Deep Learning Hardware & Software Landscape

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L2.3 The Origins of Deep Learning

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L2.1 Artificial Neurons

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L2.2 Multilayer Networks

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L2.0 A Brief History of Deep Learning -- Lecture Overview

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L1.6 About the Practical Aspects and Tools Used in This Course

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L1.5 Necessary Machine Learning Notation and Jargon

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L1.4 The Supervised Learning Workflow

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L1.3.4 Broad Categories of ML Part 4: Special Cases of Supervised Learning

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L1.3.3 Broad Categories of ML Part 3: Reinforcement Learning

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L1.3.2 Broad Categories of ML Part 2: Unsupervised Learning

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L1.3.1 Broad Categories of ML Part 1: Supervised Learning

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L1.2 What is Machine Learning?

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L1.1.2 Course Overview Part 2: Organization (Optional)

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L1.1.1 Course Overview Part 1: Motivation and Topics

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L1.0 Intro to Deep Learning, Course Introduction

Sebastian Raschka Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

Albumentations Tutorial for Data Augmentation (Pytorch focused)

Aladdin Persson Beginner 5y ago

📚 Coursera Courses Opens on Coursera · Free to audit

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Artificial Intelligence for Robotics

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Recommender Systems: An Applied Approach using Deep Learning

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Deep Learning with Keras and Tensorflow

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Self-paced

Derivatives: a guide to calculation

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Calculus through Data & Modelling: Series and Integration

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Advanced RNN Concepts and Projects

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State Estimation and Localization for Self-Driving Cars

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Learning Deep Learning: Unit 3

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Machine Learning - Anomaly Detection via PyCaret

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Predictive Models for Financial Risk

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Modelos predictivos con Machine Learning

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Deep Learning RNN & LSTM: Stock Price Prediction

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Self-paced

Introduction to RNN and DNN

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Data Science Decisions in Time: Using Causal Information

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Compare Models with Experiments in Azure ML Studio

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Python: Istruzioni per l’uso

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Abstracting with Functions

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Natural Language Processing with Classification and Vector Spaces

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