ML Fundamentals

L12.6 Additional Topics and Research on Optimization Algorithms

L12.5 Choosing Different Optimizers in PyTorch

Day 1- MLOPS End To End Implementation- Machine Learning

CycleGAN implementation from scratch

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

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

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

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

Expectations with Machine Learning

PyTorch Time Sequence Prediction With LSTM - Forecasting Tutorial

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

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

Machine Learning Frameworks - The Landscape

How to Learn DATA SCIENCE Ridiculously FAST

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

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

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

Intro to Deep Learning (ML Tech Talks)

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

L12.4 Adam: Combining Adaptive Learning Rates and Momentum

L12.3 SGD with Momentum

L12.2 Learning Rate Schedulers in PyTorch

L12.1 Learning Rate Decay

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

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

L10.4 L2 Regularization for Neural Nets

L10.2 Data Augmentation in PyTorch

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

Pix2Pix implementation from scratch

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