ML Fundamentals

L12.1 Learning Rate Decay

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

Day 1- MLOPS End To End Implementation- Machine Learning

Digital Platforms: Saints or Sinners?

CycleGAN implementation from scratch

The Discovery That Transformed Pi

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

How Shazam Works (Probably!) - Computerphile

Me he CAPTURADO en 3D... ¡DENTRO de una Red Neuronal! (y tú también puedes 👀)

Oxford Africa Business Alliance Student Webinar: Oxford MBA

Coding SVM Kernels : Data Science Code

How you SHOULD code Machine Learning

Artificial intelligence Or Machine Learning #Shorts

Will AI replace network engineers?

Convolutional Autoencoder for Image Denoising - Keras Code Examples

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

TensorFlow DCGAN Tutorial

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

MLOps Live Community Session Announcement

Dave Selinger — AI and the Next Generation of Security Systems

Code With Me : Logistic Regression (from scratch) !

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

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

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

When Unix Landed - Computerphile

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

Expectations with Machine Learning

L11.7 Weight Initialization in PyTorch -- Code Example

L11.6 Xavier Glorot and Kaiming He Initialization

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

L11.4 Why BatchNorm Works

L11.3 BatchNorm in PyTorch -- Code Example

L11.2 How BatchNorm Works

L11.1 Input Normalization

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

Waste Classification Machine Learning Classification Project-Waste Recycling

CycleGAN Paper Walkthrough

L10.5.4 Dropout in PyTorch

L10.5.3 (Optional) Dropout Ensemble Interpretation

L10.5.2 Dropout Co-Adaptation Interpretation

L10.5.1 The Main Concept Behind Dropout

L10.4 L2 Regularization for Neural Nets

L10.3 Early Stopping

L10.2 Data Augmentation in PyTorch

L10.1 Techniques for Reducing Overfitting

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

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

Pix2Pix implementation from scratch