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📐 ML Fundamentals

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

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W&B Deep Learning Salon - SafeLife & DeepForm
ML Fundamentals
W&B Deep Learning Salon - SafeLife & DeepForm
Weights & Biases Beginner 5y ago
11.6 Nested CV for Algorithm Selection Code Example (L11 Model Eval. Part 4)
ML Fundamentals
11.6 Nested CV for Algorithm Selection Code Example (L11 Model Eval. Part 4)
Sebastian Raschka Beginner 5y ago
11.5 Nested CV for Algorithm Selection (L11 Model Eval. Part 4)
ML Fundamentals
11.5 Nested CV for Algorithm Selection (L11 Model Eval. Part 4)
Sebastian Raschka Beginner 5y ago
create-ml-app - Machine Learning Project Template that Handle Dependencies
ML Fundamentals
create-ml-app - Machine Learning Project Template that Handle Dependencies
1littlecoder Beginner 5y ago
Data Science and Predictive Vehicle Maintenance with Jen | 365 Data Use Cases
ML Fundamentals
Data Science and Predictive Vehicle Maintenance with Jen | 365 Data Use Cases
365 Data Science Beginner 5y ago
Support Vector Machines : Data Science Concepts
ML Fundamentals
Support Vector Machines : Data Science Concepts
ritvikmath Beginner 5y ago
Transfer Learning | Deep Learning Tutorial 27 (Tensorflow, Keras & Python)
ML Fundamentals
Transfer Learning | Deep Learning Tutorial 27 (Tensorflow, Keras & Python)
codebasics Beginner 5y ago
Kite: Free AI Coding Assistant + Giveaway
ML Fundamentals
Kite: Free AI Coding Assistant + Giveaway
Data Professor Beginner 5y ago
Neural Networks Pt. 3: ReLU In Action!!!
ML Fundamentals
Neural Networks Pt. 3: ReLU In Action!!!
StatQuest with Josh Starmer Beginner 5y ago
Leaf Disease Classification Using PyTorch
ML Fundamentals
Leaf Disease Classification Using PyTorch
Abhishek Thakur Beginner 5y ago
PyTorch Basics and Gradient Descent | Deep Learning with PyTorch: Zero to GANs | Part 1 of 6
ML Fundamentals
PyTorch Basics and Gradient Descent | Deep Learning with PyTorch: Zero to GANs | Part 1 of 6
freeCodeCamp.org Beginner 5y ago
Build Your Own PyTorch Trainer!
ML Fundamentals
Build Your Own PyTorch Trainer!
Abhishek Thakur Beginner 5y ago
Peter Norvig – Singularity Is in the Eye of the Beholder
ML Fundamentals
Peter Norvig – Singularity Is in the Eye of the Beholder
Weights & Biases Beginner 5y ago
Supervised vs Unsupervised vs Reinforcement Learning | Machine Learning Tutorial | Simplilearn
6:27
ML Fundamentals
Supervised vs Unsupervised vs Reinforcement Learning | Machine Learning Tutorial | Simplilearn
Simplilearn Beginner 5y ago
Ineuron's Affordable BI And ML DL Course With Remote Internship From 21st November
ML Fundamentals
Ineuron's Affordable BI And ML DL Course With Remote Internship From 21st November
Krish Naik Beginner 5y ago
Convert python file to exe in less than 2 minutes (.py to .exe)
ML Fundamentals
Convert python file to exe in less than 2 minutes (.py to .exe)
codebasics Beginner 5y ago
Break into AI: I took an online course, what's next?
ML Fundamentals
Break into AI: I took an online course, what's next?
DeepLearningAI Beginner 5y ago
Data Science and Employee Productivity with Nicki | 365 Data Use Cases
ML Fundamentals
Data Science and Employee Productivity with Nicki | 365 Data Use Cases
365 Data Science Beginner 5y ago
Impute missing values using KNNImputer or IterativeImputer
ML Fundamentals
Impute missing values using KNNImputer or IterativeImputer
Data School Beginner 5y ago
Loss Functions : Data Science Basics
ML Fundamentals
Loss Functions : Data Science Basics
ritvikmath Beginner 5y ago
Build a Voice Assistant using Javascript w/Tensorflow | For Beginners
ML Fundamentals
Build a Voice Assistant using Javascript w/Tensorflow | For Beginners
CoderOne Beginner 5y ago
Getting ready to learn Python, Windows edition #5: Writing and running Python program
ML Fundamentals
Getting ready to learn Python, Windows edition #5: Writing and running Python program
Brandon Rohrer Beginner 5y ago
Getting ready to learn Python, Windows edition #4: Installing and running Python
ML Fundamentals
Getting ready to learn Python, Windows edition #4: Installing and running Python
Brandon Rohrer Beginner 5y ago
Osmo Wonderland - Perfect Christmas Gift For Kids
ML Fundamentals
Osmo Wonderland - Perfect Christmas Gift For Kids
NPStation Beginner 5y ago
Pytorch Conditional GAN Tutorial
ML Fundamentals
Pytorch Conditional GAN Tutorial
Aladdin Persson Beginner 5y ago
Set a "random_state" to make your code reproducible
ML Fundamentals
Set a "random_state" to make your code reproducible
Data School Beginner 5y ago
Ep#2 - What are regulations saying about data privacy?
ML Fundamentals
Ep#2 - What are regulations saying about data privacy?
MLOps.community Beginner 5y ago
11.4 Statistical Tests for Algorithm Comparison (L11 Model Eval. Part 4)
ML Fundamentals
11.4 Statistical Tests for Algorithm Comparison (L11 Model Eval. Part 4)
Sebastian Raschka Beginner 5y ago
11.3 Multiple Pairwise Comparisons (L11 Model Eval. Part 4)
ML Fundamentals
11.3 Multiple Pairwise Comparisons (L11 Model Eval. Part 4)
Sebastian Raschka Beginner 5y ago
11.2 McNemar's Test for Pairwise Classifier Comparison (L11 Model Eval. Part 4)
ML Fundamentals
11.2 McNemar's Test for Pairwise Classifier Comparison (L11 Model Eval. Part 4)
Sebastian Raschka Beginner 5y ago
11.1 Lecture Overview (L11 Model Eval. Part 4)
ML Fundamentals
11.1 Lecture Overview (L11 Model Eval. Part 4)
Sebastian Raschka Beginner 5y ago
10.8 K-fold CV 1-Standard Error Method -- Code Example (L10: Model Evaluation 3)
ML Fundamentals
10.8 K-fold CV 1-Standard Error Method -- Code Example (L10: Model Evaluation 3)
Sebastian Raschka Beginner 5y ago
10.7 K-fold CV 1-Standard Error Method (L10: Model Evaluation 3)
ML Fundamentals
10.7 K-fold CV 1-Standard Error Method (L10: Model Evaluation 3)
Sebastian Raschka Beginner 5y ago
10.6 K-fold CV for Model Evaluation -- Code Examples (L10: Model Evaluation 3)
ML Fundamentals
10.6 K-fold CV for Model Evaluation -- Code Examples (L10: Model Evaluation 3)
Sebastian Raschka Beginner 5y ago
10.5 K-fold CV for Model Selection (L10: Model Evaluation 3)
ML Fundamentals
10.5 K-fold CV for Model Selection (L10: Model Evaluation 3)
Sebastian Raschka Beginner 5y ago
10.4 K-fold CV for Model Evaluation -- Code Examples (L10: Model Evaluation 3)
ML Fundamentals
10.4 K-fold CV for Model Evaluation -- Code Examples (L10: Model Evaluation 3)
Sebastian Raschka Beginner 5y ago
10.3 K-fold CV for Model Evaluation (L10: Model Evaluation 3)
ML Fundamentals
10.3 K-fold CV for Model Evaluation (L10: Model Evaluation 3)
Sebastian Raschka Beginner 5y ago
10.2 Hyperparameters (L10: Model Evaluation 3)
ML Fundamentals
10.2 Hyperparameters (L10: Model Evaluation 3)
Sebastian Raschka Beginner 5y ago
10.1 Cross-validation Lecture Overview (L10: Model Evaluation 3)
ML Fundamentals
10.1 Cross-validation Lecture Overview (L10: Model Evaluation 3)
Sebastian Raschka Beginner 5y ago
Getting ready to learn Python, Windows edition #3: Creating and editing text files
ML Fundamentals
Getting ready to learn Python, Windows edition #3: Creating and editing text files
Brandon Rohrer Beginner 5y ago
Getting ready to learn Python, Windows edition #2: The command prompt
ML Fundamentals
Getting ready to learn Python, Windows edition #2: The command prompt
Brandon Rohrer Beginner 5y ago
9.7 The .632 and .632+ Bootstrap methods (L09 Model Eval 2: Confidence Intervals)
ML Fundamentals
9.7 The .632 and .632+ Bootstrap methods (L09 Model Eval 2: Confidence Intervals)
Sebastian Raschka Beginner 5y ago
9.6 Bootstrap Confidence Intervals (L09 Model Eval 2: Confidence Intervals)
ML Fundamentals
9.6 Bootstrap Confidence Intervals (L09 Model Eval 2: Confidence Intervals)
Sebastian Raschka Beginner 5y ago
Talks # 14: Martin Henze; Knowledge is Power: Understanding your Data through EDA and Visualisations
ML Fundamentals
Talks # 14: Martin Henze; Knowledge is Power: Understanding your Data through EDA and Visualisations
Abhishek Thakur Beginner 5y ago
9.5 Resampling and Repeated Holdout (L09 Model Eval 2: Confidence Intervals)
ML Fundamentals
9.5 Resampling and Repeated Holdout (L09 Model Eval 2: Confidence Intervals)
Sebastian Raschka Beginner 5y ago
9.4 ML Confidence Intervals via Normal Approximation (L09 Model Eval 2: Confidence Intervals)
ML Fundamentals
9.4 ML Confidence Intervals via Normal Approximation (L09 Model Eval 2: Confidence Intervals)
Sebastian Raschka Beginner 5y ago
9.3 Holdout Model Selection (L09 Model Eval 2: Confidence Intervals)
ML Fundamentals
9.3 Holdout Model Selection (L09 Model Eval 2: Confidence Intervals)
Sebastian Raschka Beginner 5y ago
Getting ready to learn Python, Windows edition #1: Files and directories
ML Fundamentals
Getting ready to learn Python, Windows edition #1: Files and directories
Brandon Rohrer Beginner 5y ago
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Four Rare Machine Learning Skills All Data Scientists Need
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Four Rare Machine Learning Skills All Data Scientists Need
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Optimize Deep Learning Models for Peak AI
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Optimize Deep Learning Models for Peak AI
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Data Science Companion
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Self-paced
Data Science Companion
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Introduction to Generative AI - Art of the Possible
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Self-paced
Introduction to Generative AI - Art of the Possible
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KALKÜLÜS III: UYGULAMALAR / CALCULUS III: APPLICATIONS
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KALKÜLÜS III: UYGULAMALAR / CALCULUS III: APPLICATIONS
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AI Workflow: Machine Learning, Visual Recognition and NLP
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AI Workflow: Machine Learning, Visual Recognition and NLP
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