📐 ML Fundamentals

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

ML Fundamentals

Follow These Playlist Before Interviews- Guide to Crack Data Science Interviews

Krish Naik Beginner 5y ago

ML Fundamentals

Learn NumPy In 30 Minutes

Patrick Loeber Beginner 5y ago

ML Fundamentals

AI Face Body and Hand Pose Detection with Python and Mediapipe

Nicholas Renotte Beginner 5y ago

ML Fundamentals

Rainfall Prediction- Converting A Kaggle Project to End To End Machine Learning Project

Krish Naik Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

Join with me to attend FREE AI Conference NVIDIA GTC: April 12 to 16

codebasics Beginner 5y ago

ML Fundamentals

AI+X: AI Innovation in Healthcare

DeepLearningAI Beginner 5y ago

ML Fundamentals

A Chat with Andrew on MLOps: From Model-centric to Data-centric AI

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ML Fundamentals

Word embedding using keras embedding layer | Deep Learning Tutorial 40 (Tensorflow, Keras & Python)

codebasics Beginner 5y ago

ML Fundamentals

Oxford High Performance Leadership Programme | Ethos and Virtual Design

Saïd Business School, University of Oxford Advanced 5y ago

ML Fundamentals

L13.9.3 AlexNet in PyTorch

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L13.9.2 Saving and Loading Models in PyTorch

Sebastian Raschka Beginner 5y ago

ML Fundamentals

Project InnerEye: Augmenting cancer radiotherapy workflows with deep learning and open source

Microsoft Research Advanced 5y ago

ML Fundamentals

ProGAN implementation from scratch

Aladdin Persson Beginner 5y ago

ML Fundamentals

Build by Small Pieces // Igor Lushchyk // MLOps Meetup #55 short clip

MLOps.community Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

Autoencoder In PyTorch - Theory & Implementation

Patrick Loeber Beginner 5y ago

ML Fundamentals

Operationalizing Machine Learning at a Large Financial Institution // Daniel Stahl //MLOps Meetup#56

MLOps.community Beginner 5y ago

ML Fundamentals

MARCH MADNESS - Will My Machine Learning Model Beat Your Bracket?

Ken Jee Beginner 5y ago

ML Fundamentals

ProGAN Paper Walkthrough

Aladdin Persson Beginner 5y ago

ML Fundamentals

Should You Scale Your Data ??? : Data Science Concepts

ritvikmath Intermediate 5y ago

ML Fundamentals

The Discovery That Transformed Pi

Veritasium Advanced 5y ago

ML Fundamentals

Learn TensorFlow and Deep Learning fundamentals with Python (code-first introduction) Part 1/2

Daniel Bourke Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

How Shazam Works (Probably!) - Computerphile

Computerphile Intermediate 5y ago

ML Fundamentals

Friday Live Q&A Ask Anything RelatedData Science

Krish Naik Beginner 5y ago

ML Fundamentals

Colab Pro Now Available In India, Brazil, France, Thailand,Japan,UK- BOON FOR Data Science Aspirants

Krish Naik Advanced 5y ago

ML Fundamentals

Successful Career Transition Story From Fresher College Student To Data Science-Sameer Singh

Krish Naik Beginner 5y ago

ML Fundamentals

Amazing Initiative For School Kids By iNeuron

Krish Naik Beginner 5y ago

ML Fundamentals

Texthero-Text Preprocessing, Representation And Visualization From Zero to Hero.

Krish Naik Beginner 5y ago

ML Fundamentals

L13.9.1 LeNet-5 in PyTorch

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L13.8 What a CNN Can See

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L13.7 CNN Architectures & AlexNet

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L13.6 CNNs & Backpropagation

Sebastian Raschka Beginner 5y ago

ML Fundamentals

Lux - Python Library for Intelligent Visual Discovery

Krish Naik Beginner 5y ago

ML Fundamentals

Devops Vs MLOPS- Understand The Differences And Why IT is Important

Krish Naik Beginner 5y ago

ML Fundamentals

Day 4- MLOPS Continuous Integration And Model Tracking Using MLFlow- Machine Learning

Krish Naik Beginner 5y ago

ML Fundamentals

L13.4 Convolutional Filters and Weight-Sharing

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L13.3 Convolutional Neural Network Basics

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L13.0 Introduction to Convolutional Networks -- Lecture Overview

Sebastian Raschka Beginner 5y ago

ML Fundamentals

Day 3- MLOPS End To End Implementation With Deployment- Machine Learning

Krish Naik Beginner 5y ago

ML Fundamentals

Day 2- MLOPS End To End Implementation From Basics- Machine Learning

Krish Naik Beginner 5y ago

ML Fundamentals

L12.6 Additional Topics and Research on Optimization Algorithms

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L12.5 Choosing Different Optimizers in PyTorch

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L12.4 Adam: Combining Adaptive Learning Rates and Momentum

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L12.3 SGD with Momentum

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L12.2 Learning Rate Schedulers in PyTorch

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L12.1 Learning Rate Decay

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L12.0: Improving Gradient Descent-based Optimization -- Lecture Overview

Sebastian Raschka Beginner 5y ago

ML Fundamentals

Day 1- MLOPS End To End Implementation- Machine Learning

Krish Naik Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

CycleGAN implementation from scratch

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📚 Coursera Courses Opens on Coursera · Free to audit

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Self-paced

ML Pipelines on Google Cloud - Français

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Machine Learning – Modern Computer Vision & Generative AI

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Using Machine Learning in Trading and Finance

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Data Science Challenge

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Introduction to Machine Learning in Sports Analytics

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Create (Artificial Intelligence) AI VIDEOS WITH ZERO FILMING

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Excel: Apply & Evaluate Unsupervised Clustering

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AI-First Test Automation with Functionize Essentials

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Deep Learning with Keras and Tensorflow

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Abstracting with Functions

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Data Science Fundamentals Part 1: Unit 1

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📚 Coursera Course ↗

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Physics 101 - Forces and Kinematics

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Build Decision Trees, SVMs, and Artificial Neural Networks

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Deep Learning Frameworks and Neural Networks Simplified

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Microsoft Gen AI for Executives Course - Gen AI Adoption

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Advanced Data Structures & Algorithms in Practice

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Technologies and platforms for Artificial Intelligence

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Data Engineering in AWS

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