📐 ML Fundamentals

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

ML Fundamentals

Will AI replace network engineers?

David Bombal Beginner 5y ago

ML Fundamentals

Convolutional Autoencoder for Image Denoising - Keras Code Examples

Connor Shorten Beginner 5y ago

ML Fundamentals

Converting words to numbers, Word Embeddings | Deep Learning Tutorial 39 (Tensorflow & Python)

codebasics Beginner 5y ago

ML Fundamentals

TensorFlow DCGAN Tutorial

Aladdin Persson Beginner 5y ago

ML Fundamentals

How to Avoid Suffering in MLOps/Data Engineering Role // Igor Lushchyk // MLOps Meetup #55

MLOps.community Beginner 5y ago

ML Fundamentals

MLOps Live Community Session Announcement

Krish Naik Beginner 5y ago

ML Fundamentals

L11.7 Weight Initialization in PyTorch -- Code Example

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L11.6 Xavier Glorot and Kaiming He Initialization

Sebastian Raschka Beginner 5y ago

ML Fundamentals

Dave Selinger — AI and the Next Generation of Security Systems

Weights & Biases Beginner 5y ago

ML Fundamentals

Waste Classification Machine Learning Classification Project-Waste Recycling

Krish Naik Beginner 5y ago

ML Fundamentals

CycleGAN Paper Walkthrough

Aladdin Persson Beginner 5y ago

ML Fundamentals

Code With Me : Logistic Regression (from scratch) !

ritvikmath Intermediate 5y ago

ML Fundamentals

[AI Access] Applied Analytics from End-to-End

DeepLearningAI Intermediate 5y ago

ML Fundamentals

Building World-Class NLP Models with Transformers and Hugging Face | Grandmaster Series E4

NVIDIA Developer Advanced 5y ago

ML Fundamentals

The Great Decoupling? The Future of Relations between China and the West

Saïd Business School, University of Oxford Advanced 5y ago

ML Fundamentals

Directions in ML: Taking Advantage of Randomness in Expensive Optimization Problems

Microsoft Research Advanced 5y ago

ML Fundamentals

When Unix Landed - Computerphile

Computerphile Intermediate 5y ago

ML Fundamentals

AI and Gaming Research Summit 2021 - Understanding Players (Day 2 Track 1.2)

Microsoft Research Beginner 5y ago

ML Fundamentals

Expectations with Machine Learning

CodeEmporium Beginner 5y ago

ML Fundamentals

PyTorch Time Sequence Prediction With LSTM - Forecasting Tutorial

Patrick Loeber Beginner 5y ago

ML Fundamentals

International Women's Day 2021 - A message from Dean Peter Tufano

Saïd Business School, University of Oxford Intermediate 5y ago

ML Fundamentals

Level up your software engineering skills as a data scientist | ML Monthly February 2021

Daniel Bourke Beginner 5y ago

ML Fundamentals

Neural Networks Part 8: Image Classification with Convolutional Neural Networks (CNNs)

StatQuest with Josh Starmer Beginner 5y ago

ML Fundamentals

Machine Learning Frameworks - The Landscape

Roboflow Beginner 5y ago

ML Fundamentals

How to Learn DATA SCIENCE Ridiculously FAST

Nicholas Renotte Beginner 5y ago

ML Fundamentals

Python Tutorial for Beginners - Learn Python in 5 Hours [FULL COURSE]

TechWorld with Nana Beginner 5y ago

ML Fundamentals

PIE & AI Kigali – Building a Career in Data Science: From Learning to Landing a Job

Deep Learning Revision Beginner 5y ago

ML Fundamentals

Cal Newport: Deep Work, Focus, Productivity, Email, and Social Media | Lex Fridman Podcast #166

Lex Fridman Beginner 5y ago

ML Fundamentals

Intro to Deep Learning (ML Tech Talks)

TensorFlow Beginner 5y ago

ML Fundamentals

Does anyone know of a good way to manage dependencies? // MLOps Coffee Sessions #31 Part 2 Clip

MLOps.community Beginner 5y ago

ML Fundamentals

L11.5 Weight Initialization -- Why Do We Care?

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L11.4 Why BatchNorm Works

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L11.3 BatchNorm in PyTorch -- Code Example

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L11.2 How BatchNorm Works

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L11.1 Input Normalization

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L11.0 Input Normalization and Weight Initialization -- Lecture Overview

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L10.5.4 Dropout in PyTorch

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L10.5.3 (Optional) Dropout Ensemble Interpretation

Sebastian Raschka Intermediate 5y ago

ML Fundamentals

L10.5.2 Dropout Co-Adaptation Interpretation

Sebastian Raschka Intermediate 5y ago

ML Fundamentals

L10.5.1 The Main Concept Behind Dropout

Sebastian Raschka Intermediate 5y ago

ML Fundamentals

L10.4 L2 Regularization for Neural Nets

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L10.3 Early Stopping

Sebastian Raschka Intermediate 5y ago

ML Fundamentals

L10.2 Data Augmentation in PyTorch

Sebastian Raschka Beginner 5y ago

ML Fundamentals

L10.1 Techniques for Reducing Overfitting

Sebastian Raschka Intermediate 5y ago

ML Fundamentals

L10.0 Regularization Methods for Neural Networks -- Lecture Overview

Sebastian Raschka Beginner 5y ago

ML Fundamentals

Pix2Pix implementation from scratch

Aladdin Persson Beginner 5y ago

ML Fundamentals

How (and why) you should undervolt your GPU - A step by step guide (Deep Learning/Gaming/Mining)

Aladdin Persson Beginner 5y ago

ML Fundamentals

Live AWS For Data Science - Deploying Machine Learning Application In EC2 Instance

Krish Naik Intermediate 5y ago