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📐 ML Fundamentals

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

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How Shazam Works (Probably!) - Computerphile
📐 ML Fundamentals
How Shazam Works (Probably!) - Computerphile
Computerphile Intermediate 5y ago
Oxford Africa Business Alliance Student Webinar: Oxford MBA
📐 ML Fundamentals
Oxford Africa Business Alliance Student Webinar: Oxford MBA
Saïd Business School, University of Oxford Intermediate 5y ago
Coding SVM Kernels : Data Science Code
📐 ML Fundamentals
Coding SVM Kernels : Data Science Code
ritvikmath Intermediate 5y ago
Code With Me : Logistic Regression (from scratch) !
📐 ML Fundamentals
Code With Me : Logistic Regression (from scratch) !
ritvikmath Intermediate 5y ago
[AI Access] Applied Analytics from End-to-End
📐 ML Fundamentals
[AI Access] Applied Analytics from End-to-End
DeepLearningAI Intermediate 5y ago
L10.5.3 (Optional) Dropout Ensemble Interpretation
📐 ML Fundamentals
L10.5.3 (Optional) Dropout Ensemble Interpretation
Sebastian Raschka Intermediate 5y ago
L10.5.2 Dropout Co-Adaptation Interpretation
📐 ML Fundamentals
L10.5.2 Dropout Co-Adaptation Interpretation
Sebastian Raschka Intermediate 5y ago
When Unix Landed - Computerphile
📐 ML Fundamentals
When Unix Landed - Computerphile
Computerphile Intermediate 5y ago
International Women's Day 2021 - A message from Dean Peter Tufano
📐 ML Fundamentals
International Women's Day 2021 - A message from Dean Peter Tufano
Saïd Business School, University of Oxford Intermediate 5y ago
Live AWS For Data Science - Deploying Machine Learning Application In EC2 Instance
📐 ML Fundamentals
Live AWS For Data Science - Deploying Machine Learning Application In EC2 Instance
Krish Naik Intermediate 5y ago
MIT OpenCourseWare: Origins, Pathways, and Possibilities
📐 ML Fundamentals
MIT OpenCourseWare: Origins, Pathways, and Possibilities
MIT OpenCourseWare Intermediate 5y ago
Monday Night Live Q&A - Ask Anything Related To Data Science
📐 ML Fundamentals
Monday Night Live Q&A - Ask Anything Related To Data Science
Krish Naik Intermediate 5y ago
Metric Learning for Images - Keras Code Examples
📐 ML Fundamentals
Metric Learning for Images - Keras Code Examples
Connor Shorten Intermediate 5y ago
Expert Panel: Optimizing BizOps with AI
📐 ML Fundamentals
Expert Panel: Optimizing BizOps with AI
DeepLearningAI Intermediate 5y ago
Javier Ideami on Loss Landscape Visualizations - W&B Salon
📐 ML Fundamentals
Javier Ideami on Loss Landscape Visualizations - W&B Salon
Weights & Biases Intermediate 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
Democratize AI with OneAPI
📐 ML Fundamentals
Democratize AI with OneAPI
The New Stack Intermediate 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
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
Build a 2D convolutional neural network, part 16: Cottonwood code tour
Brandon Rohrer Intermediate 5y ago
L10.5.1 The Main Concept Behind Dropout
📐 ML Fundamentals
L10.5.1 The Main Concept Behind Dropout
Sebastian Raschka Intermediate 5y ago
L10.3 Early Stopping
📐 ML Fundamentals
L10.3 Early Stopping
Sebastian Raschka Intermediate 5y ago
L10.1 Techniques for Reducing Overfitting
📐 ML Fundamentals
L10.1 Techniques for Reducing Overfitting
Sebastian Raschka Intermediate 5y ago
Radial Basis Function Kernel : Data Science Concepts
📐 ML Fundamentals
Radial Basis Function Kernel : Data Science Concepts
ritvikmath Intermediate 5y ago
Leadership in Extraordinary Times S3E2: Navigating the next normal: a view from female leaders
📐 ML Fundamentals
Leadership in Extraordinary Times S3E2: Navigating the next normal: a view from female leaders
Saïd Business School, University of Oxford Intermediate 5y ago
SVM Kernels : Data Science Concepts
📐 ML Fundamentals
SVM Kernels : Data Science Concepts
ritvikmath Intermediate 5y ago
Chacha Cipher - Computerphile
📐 ML Fundamentals
Chacha Cipher - Computerphile
Computerphile Intermediate 5y ago
PerceptiLabs-The Best Machine Learning Visual Modeling Tool-Train Deep Learning Neural Network
📐 ML Fundamentals
PerceptiLabs-The Best Machine Learning Visual Modeling Tool-Train Deep Learning Neural Network
Krish Naik Intermediate 5y ago
How capital markets can take sustainability to the next level
📐 ML Fundamentals
How capital markets can take sustainability to the next level
Saïd Business School, University of Oxford Intermediate 5y ago
Session On Different Types Of Loss Function In Deep Learning
📐 ML Fundamentals
Session On Different Types Of Loss Function In Deep Learning
Krish Naik Intermediate 5y ago
Coding MCMC : Data Science Code
📐 ML Fundamentals
Coding MCMC : Data Science Code
ritvikmath Intermediate 5y ago
Sunday Late Night Live Q&A - Ask Anything Related To Data Science
📐 ML Fundamentals
Sunday Late Night Live Q&A - Ask Anything Related To Data Science
Krish Naik Intermediate 5y ago
Ubicomp (Ubiquitous Computing) - Computerphile
📐 ML Fundamentals
Ubicomp (Ubiquitous Computing) - Computerphile
Computerphile Intermediate 5y ago
Navigating the next normal: A view from female leaders
📐 ML Fundamentals
Navigating the next normal: A view from female leaders
Saïd Business School, University of Oxford Intermediate 5y ago
Bayesian Treasure Hunt : Data Science Code
📐 ML Fundamentals
Bayesian Treasure Hunt : Data Science Code
ritvikmath Intermediate 5y ago
Build a 2D convolutional neural network, part 15: Rendering examples
📐 ML Fundamentals
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
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
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
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
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
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
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Modeling in AWS
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Mathematical Thinking in Computer Science
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Calculus through Data & Modelling: Vector Calculus
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Calculus through Data & Modelling: Vector Calculus
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Creative AI: Sound, Music and Interaction
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Creative AI: Sound, Music and Interaction
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Responsible AI: Applying AI Principles with Google Cloud
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Responsible AI: Applying AI Principles with Google Cloud
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GenAI for Risk Managers: Advanced Risk Analysis Techniques
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GenAI for Risk Managers: Advanced Risk Analysis Techniques
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