📐 ML Fundamentals

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

ML Fundamentals

PyTorch Basics and Gradient Descent | Deep Learning with PyTorch: Zero to GANs | Part 1 of 6

freeCodeCamp.org Beginner 5y ago

ML Fundamentals

Build Your Own PyTorch Trainer!

Abhishek Thakur Beginner 5y ago

ML Fundamentals

10.8 K-fold CV 1-Standard Error Method -- Code Example (L10: Model Evaluation 3)

Sebastian Raschka Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

10.7 K-fold CV 1-Standard Error Method (L10: Model Evaluation 3)

Sebastian Raschka Beginner 5y ago

ML Fundamentals

Peter Norvig – Singularity Is in the Eye of the Beholder

Weights & Biases Beginner 5y ago

6:27

ML Fundamentals ⚡ AI Lesson

Supervised vs Unsupervised vs Reinforcement Learning | Machine Learning Tutorial | Simplilearn

Simplilearn Beginner 5y ago

ML Fundamentals

Ineuron's Affordable BI And ML DL Course With Remote Internship From 21st November

Krish Naik Beginner 5y ago

ML Fundamentals

Convert python file to exe in less than 2 minutes (.py to .exe)

codebasics Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

Break into AI: I took an online course, what's next?

DeepLearningAI Beginner 5y ago

ML Fundamentals

Data Science and Employee Productivity with Nicki | 365 Data Use Cases

365 Data Science Beginner 5y ago

ML Fundamentals

Impute missing values using KNNImputer or IterativeImputer

Data School Beginner 5y ago

ML Fundamentals

Loss Functions : Data Science Basics

ritvikmath Beginner 5y ago

ML Fundamentals

Build a Voice Assistant using Javascript w/Tensorflow | For Beginners

CoderOne Beginner 5y ago

ML Fundamentals

Getting ready to learn Python, Windows edition #5: Writing and running Python program

Brandon Rohrer Beginner 5y ago

ML Fundamentals

Getting ready to learn Python, Windows edition #4: Installing and running Python

Brandon Rohrer Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

Talks # 14: Martin Henze; Knowledge is Power: Understanding your Data through EDA and Visualisations

Abhishek Thakur Beginner 5y ago

ML Fundamentals

Osmo Wonderland - Perfect Christmas Gift For Kids

NPStation Beginner 5y ago

ML Fundamentals

Pytorch Conditional GAN Tutorial

Aladdin Persson Beginner 5y ago

ML Fundamentals

Set a "random_state" to make your code reproducible

Data School Beginner 5y ago

ML Fundamentals

Ep#2 - What are regulations saying about data privacy?

MLOps.community Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

How Deep Learning has Revolutionized OCR with Cha Zhang - #416

The TWIML AI Podcast with Sam Charrington Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

The ROC Curve : Data Science Concepts

ritvikmath Beginner 5y ago

ML Fundamentals

Flow Physics Quantification in an Aneurysm Using NVIDIA PhysicsNeMo

NVIDIA Developer Beginner 5y ago

ML Fundamentals

Live at Jeff Kinney’s pool party and The Deep End book review!

NPStation Beginner 5y ago

ML Fundamentals

Joe Spisak-Deep Learning Development with PyTorch+Jupyter using Heterogenous hardware|JupyterCon2020

JupyterCon Beginner 5y ago

ML Fundamentals

Important Steps I Have Followed To Improve My Data Science Skills- Sharing My Experience

Krish Naik Beginner 5y ago

ML Fundamentals

When Machine Learning meets privacy - Episode 1

MLOps.community Beginner 5y ago

ML Fundamentals

This Book will Help you Land a Data Science Job

Data Professor Beginner 5y ago

ML Fundamentals

10.6 K-fold CV for Model Evaluation -- Code Examples (L10: Model Evaluation 3)

Sebastian Raschka Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

10.5 K-fold CV for Model Selection (L10: Model Evaluation 3)

Sebastian Raschka Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

10.4 K-fold CV for Model Evaluation -- Code Examples (L10: Model Evaluation 3)

Sebastian Raschka Beginner 5y ago

ML Fundamentals

10.3 K-fold CV for Model Evaluation (L10: Model Evaluation 3)

Sebastian Raschka Beginner 5y ago

ML Fundamentals

10.2 Hyperparameters (L10: Model Evaluation 3)

Sebastian Raschka Beginner 5y ago

ML Fundamentals

10.1 Cross-validation Lecture Overview (L10: Model Evaluation 3)

Sebastian Raschka Beginner 5y ago

ML Fundamentals

Getting ready to learn Python, Windows edition #3: Creating and editing text files

Brandon Rohrer Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

Getting ready to learn Python, Windows edition #2: The command prompt

Brandon Rohrer Beginner 5y ago

ML Fundamentals

9.7 The .632 and .632+ Bootstrap methods (L09 Model Eval 2: Confidence Intervals)

Sebastian Raschka Beginner 5y ago

ML Fundamentals

9.6 Bootstrap Confidence Intervals (L09 Model Eval 2: Confidence Intervals)

Sebastian Raschka Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

9.5 Resampling and Repeated Holdout (L09 Model Eval 2: Confidence Intervals)

Sebastian Raschka Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

9.4 ML Confidence Intervals via Normal Approximation (L09 Model Eval 2: Confidence Intervals)

Sebastian Raschka Beginner 5y ago

ML Fundamentals

9.3 Holdout Model Selection (L09 Model Eval 2: Confidence Intervals)

Sebastian Raschka Beginner 5y ago

ML Fundamentals

Getting ready to learn Python, Windows edition #1: Files and directories

Brandon Rohrer Beginner 5y ago

ML Fundamentals

9.2 Holdout Evaluation (L09 Model Eval 2: Confidence Intervals)

Sebastian Raschka Beginner 5y ago

ML Fundamentals

9.1 Introduction (L09 Model Eval 2: Confidence Intervals)

Sebastian Raschka Beginner 5y ago

ML Fundamentals

Add a missing indicator to encode "missingness" as a feature

Data School Beginner 5y ago

ML Fundamentals

8.5 Bias-Variance Decomposition of the 0/1 Loss (L08: Model Evaluation Part 1)

Sebastian Raschka Beginner 5y ago

ML Fundamentals

Use Pipeline to chain together multiple steps

Data School Beginner 5y ago

ML Fundamentals

Why There Are So Many Start Ups In AI, ML And DL? Important For Everyone

Krish Naik Beginner 5y ago

📚 Coursera Courses Opens on Coursera · Free to audit

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Apply Generative Adversarial Networks (GANs)

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Build Regression, Classification, and Clustering Models

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Prediction and Control with Function Approximation

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Calculus through Data & Modelling: Integration Applications

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Foundations of No-Code AI for Real-World Applications

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Deep Learning - Recurrent Neural Networks with TensorFlow

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Machine Learning and Human Learning

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Validate, Analyze, and Monitor ML Models

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Supervised Machine Learning: Regression

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Machine Learning: Random Forest with Python from Scratch©

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Sequence Modeling, Transformers, and Transfer Learning

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GenAI and Model Selection

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Machine Learning Operations with Vertex AI: Model Evaluation

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Encoder-Decoder Architecture - Português Brasileiro

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Análisis exploratorio de datos con Python y Pandas

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AI-900 - Azure AI Fundamentals and Two Practice Tests

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Microsoft Azure Machine Learning

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Foundations of Statistical Learning & Algorithms

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