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📐 ML Fundamentals

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

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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
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
Build Your Own PyTorch Trainer!
ML Fundamentals
Build Your Own PyTorch Trainer!
Abhishek Thakur Beginner 5y ago
Handling Imbalanced Dataset Using Cost Sensitive Neural Networks- Credit Card Fraud Detection
ML Fundamentals
Handling Imbalanced Dataset Using Cost Sensitive Neural Networks- Credit Card Fraud Detection
Krish Naik Intermediate 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
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
🏺 Version Control Data and Models with W&B Artifacts
ML Fundamentals
🏺 Version Control Data and Models with W&B Artifacts
Weights & Biases Intermediate 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
AdaBoost : Data Science Concepts
ML Fundamentals
AdaBoost : Data Science Concepts
ritvikmath Intermediate 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
Youth Setting the Agenda - Food and Agriculture
ML Fundamentals
Youth Setting the Agenda - Food and Agriculture
Saïd Business School, University of Oxford Intermediate 5y ago
How to Perform Large-Scale Image Classification | Grandmaster Series E2
ML Fundamentals
How to Perform Large-Scale Image Classification | Grandmaster Series E2
NVIDIA Developer Advanced 5y ago
Impute missing values using KNNImputer or IterativeImputer
ML Fundamentals
Impute missing values using KNNImputer or IterativeImputer
Data School Beginner 5y ago
Cheap talk? Living up to the Business Roundtable Statement
ML Fundamentals
Cheap talk? Living up to the Business Roundtable Statement
Saïd Business School, University of Oxford Intermediate 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
Random Boolean Networks - Computerphile
ML Fundamentals
Random Boolean Networks - Computerphile
Computerphile Intermediate 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
Debug your YOLOv5 experiments with Weights & Biases
ML Fundamentals
Debug your YOLOv5 experiments with Weights & Biases
Weights & Biases Advanced 5y ago
Loss Functions : Data Science Basics
ML Fundamentals
Loss Functions : Data Science Basics
ritvikmath Beginner 5y ago
Ineuron's Detailed Course Content Discussion Of ML And DL- Affordable AI Education
ML Fundamentals
Ineuron's Detailed Course Content Discussion Of ML And DL- Affordable AI Education
Krish Naik Intermediate 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
Robert Nishihara — The State of Distributed Computing in ML
ML Fundamentals
Robert Nishihara — The State of Distributed Computing in ML
Weights & Biases Intermediate 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
The future of recruitment
ML Fundamentals
The future of recruitment
Saïd Business School, University of Oxford Advanced 5y ago
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Cloud Data Engineering
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Data Prep for Machine Learning in Python
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State Estimation and Localization for Self-Driving Cars
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State Estimation and Localization for Self-Driving Cars
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Motion Planning for Self-Driving Cars
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Motion Planning for Self-Driving Cars
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Building a Large-Scale, Automated Forecasting System
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Building a Large-Scale, Automated Forecasting System
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Advanced RNN Concepts and Projects
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Advanced RNN Concepts and Projects
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