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📐 ML Fundamentals

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

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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
How to do the Titanic Kaggle Competition
ML Fundamentals ⚡ AI Lesson
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 ⚡ AI Lesson
What is OneAPI? The Software Tool Gap: A Roundtable Discussion
The New Stack Beginner 5y ago
Join us at TensorFlow Everywhere
ML Fundamentals ⚡ AI Lesson
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
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
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
What the Heck is Bayesian Stats ?? : Data Science Basics
ML Fundamentals
What the Heck is Bayesian Stats ?? : Data Science Basics
ritvikmath Beginner 5y ago
How does a Data Scientist Fight FRAUD?
ML Fundamentals ⚡ AI Lesson
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 ⚡ AI Lesson
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 ⚡ AI Lesson
Build a 2D convolutional neural network, part 16: Cottonwood code tour
Brandon Rohrer Intermediate 5y ago
Simple Explanation of LSTM | Deep Learning Tutorial 36 (Tensorflow, Keras & Python)
ML Fundamentals ⚡ AI Lesson
Simple Explanation of LSTM | Deep Learning Tutorial 36 (Tensorflow, Keras & Python)
codebasics 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 ⚡ AI Lesson
Intel: How Google Health Uses Machine Learning With Intel
The New Stack 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
Deep Learning News #2, Feb 6 2021
ML Fundamentals
Deep Learning News #2, Feb 6 2021
Sebastian Raschka Intermediate 5y ago
Build a 2D convolutional neural network, part 15: Rendering examples
ML Fundamentals ⚡ AI Lesson
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 ⚡ AI Lesson
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 ⚡ AI Lesson
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 ⚡ AI Lesson
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 ⚡ AI Lesson
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 ⚡ AI Lesson
Build a 2D convolutional neural network, part 10: Connecting layers
Brandon Rohrer Intermediate 5y ago
Build a 2D convolutional neural network, part 9: Adding layers
ML Fundamentals
Build a 2D convolutional neural network, part 9: Adding layers
Brandon Rohrer Intermediate 5y ago
Build a 2D convolutional neural network, part 8: Training code setup
ML Fundamentals
Build a 2D convolutional neural network, part 8: Training code setup
Brandon Rohrer Intermediate 5y ago
Build a 2D convolutional neural network, part 7: Why Cottonwood?
ML Fundamentals
Build a 2D convolutional neural network, part 7: Why Cottonwood?
Brandon Rohrer Intermediate 5y ago
Build a 2D convolutional neural network, part 6: Examples of successes and failures
ML Fundamentals
Build a 2D convolutional neural network, part 6: Examples of successes and failures
Brandon Rohrer Intermediate 5y ago
Build a 2D convolutional neural network, part 5: Pre-trained model results
ML Fundamentals
Build a 2D convolutional neural network, part 5: Pre-trained model results
Brandon Rohrer Intermediate 5y ago
Build a 2D convolutional neural network, part 4: Model overview
ML Fundamentals
Build a 2D convolutional neural network, part 4: Model overview
Brandon Rohrer Intermediate 5y ago
Build a 2D convolutional neural network, part 3: MNIST digits
ML Fundamentals
Build a 2D convolutional neural network, part 3: MNIST digits
Brandon Rohrer Intermediate 5y ago
Build a 2D convolutional neural network, part 2: Overview
ML Fundamentals
Build a 2D convolutional neural network, part 2: Overview
Brandon Rohrer Intermediate 5y ago
Build a 2D convolutional neural network, part 1: Getting started
ML Fundamentals
Build a 2D convolutional neural network, part 1: Getting started
Brandon Rohrer Intermediate 5y ago
Build a 1D convolutional neural network, part 7: Evaluate the model
ML Fundamentals
Build a 1D convolutional neural network, part 7: Evaluate the model
Brandon Rohrer Beginner 5y ago
Build a 1D convolutional neural network, part 6: Text summary and loss history
ML Fundamentals
Build a 1D convolutional neural network, part 6: Text summary and loss history
Brandon Rohrer Beginner 5y ago
Build a 1D convolutional neural network, part 5: One Hot, Flatten, and Logging blocks
ML Fundamentals
Build a 1D convolutional neural network, part 5: One Hot, Flatten, and Logging blocks
Brandon Rohrer Beginner 5y ago
Build a 1D convolutional neural network, part 4: Training, evaluation, reporting
ML Fundamentals
Build a 1D convolutional neural network, part 4: Training, evaluation, reporting
Brandon Rohrer Advanced 5y ago
Build a 1D convolutional neural network , part 3: Connect the blocks into a network structure
ML Fundamentals
Build a 1D convolutional neural network , part 3: Connect the blocks into a network structure
Brandon Rohrer Beginner 5y ago
Build a 1D convolutional neural network , part 2: Collect the Cottonwood blocks
ML Fundamentals ⚡ AI Lesson
Build a 1D convolutional neural network , part 2: Collect the Cottonwood blocks
Brandon Rohrer Beginner 5y ago
Build a 1D convolutional neural network, part 1: Create a test data set
ML Fundamentals
Build a 1D convolutional neural network, part 1: Create a test data set
Brandon Rohrer Beginner 5y ago
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Industrial Applications of AI
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Industrial Applications of AI
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VLSI CAD Part I: Logic
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VLSI CAD Part I: Logic
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RNN Architecture and Sentiment Classification
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RNN Architecture and Sentiment Classification
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Web of Data
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Web of Data
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AppDynamics Monitoring
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AppDynamics Monitoring
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Face Recognition with Keras: Detect & Classify
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Face Recognition with Keras: Detect & Classify
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