📐 ML Fundamentals

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

ML Fundamentals

L5.5 (Optional) Calculus Refresher II: Gradients

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L5.4 (Optional) Calculus Refresher I: Derivatives

Sebastian Raschka Beginner 5y ago

ML Fundamentals

Piero Molino — The Secret Behind Building Successful Open Source Projects

Weights & Biases Beginner 5y ago

ML Fundamentals

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

Data Professor Beginner 5y ago

ML Fundamentals

How to do the Titanic Kaggle Competition

Aladdin Persson Beginner 5y ago

ML Fundamentals

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

The New Stack Beginner 5y ago

ML Fundamentals

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

The New Stack Beginner 5y ago

ML Fundamentals

Join us at TensorFlow Everywhere

TensorFlow Beginner 5y ago

ML Fundamentals

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

ritvikmath Beginner 5y ago

ML Fundamentals

How does a Data Scientist Fight FRAUD?

CodeEmporium Beginner 5y ago

ML Fundamentals

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

1littlecoder Beginner 5y ago

ML Fundamentals

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

StatQuest with Josh Starmer Beginner 5y ago

ML Fundamentals

Neural Networks Part 5: ArgMax and SoftMax

StatQuest with Josh Starmer Beginner 5y ago

ML Fundamentals

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

codebasics Beginner 5y ago

ML Fundamentals

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

Brandon Rohrer Beginner 5y ago

ML Fundamentals

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

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

Rosanne Liu — Conducting Fundamental ML Research as a Nonprofit

Weights & Biases 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

L5.3 An Iterative Training Algorithm for Linear Regression

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L5.2 Relation Between Perceptron and Linear Regression

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L5.1 Online, Batch, and Minibatch Mode

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L5.0 Gradient Descent -- Lecture Overview

Sebastian Raschka Beginner 5y ago

ML Fundamentals

Intel: How Google Health Uses Machine Learning With Intel

The New Stack Beginner 5y ago

ML Fundamentals

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

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L4.4 Notational Conventions for Neural Networks

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L4.3 Vectors, Matrices, and Broadcasting

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L4.2 Tensors in PyTorch

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L4.1 Tensors in Deep Learning

Sebastian Raschka Beginner 5y ago

ML Fundamentals

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

Sebastian Raschka Beginner 5y ago

ML Fundamentals

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

Brandon Rohrer Beginner 5y ago

ML Fundamentals

Build a 1D convolutional neural network , part 3: Connect the blocks into a network structure

Brandon Rohrer Beginner 5y ago

ML Fundamentals

Build a 1D convolutional neural network , part 2: Collect the Cottonwood blocks

Brandon Rohrer Beginner 5y ago

ML Fundamentals

Build a 1D convolutional neural network, part 1: Create a test data set

Brandon Rohrer Beginner 5y ago

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

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

Brandon Rohrer Beginner 5y ago

ML Fundamentals

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

L3.1 About Brains and Neurons

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L3.5 The Geometric Intuition Behind the Perceptron

Sebastian Raschka Beginner 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

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Analyze, Create, and Secure Data with Zero Trust

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Managing Machine Learning Projects

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Machine Learning with Small Data Part 2

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Introduction to Generative AI - Português Brasileiro

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MLOps Tools: MLflow and Hugging Face

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Browser-based Models with TensorFlow.js

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Data Pipelines with TensorFlow Data Services

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Remote Sensing Image Acquisition, Analysis and Applications

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Machine Learning Models in Science

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Foundations of No-Code AI for Real-World Applications

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Intermediate Data Manipulation and Machine Learning

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Linear Algebra: Orthogonality and Diagonalization

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Planning a Machine Learning Project

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AI for Medical Diagnosis

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Linear Algebra: Linear Systems and Matrix Equations

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Reinforcement Learning for Trading Strategies

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Developing Explainable AI (XAI)

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Fundamentals of Deep Learning

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