Aladdin Persson Courses

NeRFs- Neural Radiance Fields - Paper Explained

1 rating

Explore neural radiance fields for 3D scene representation and view synthesis, covering network architecture, key concepts, learning process, and advanced techniques.

• YouTube
• 20 minutes
• Self-Paced
• Free Video

Graph Neural Networks- A Gentle Introduction

1 rating

Gentle introduction to Graph Neural Networks, covering key concepts, properties, and variants. Learn about graph representation, information propagation, and common tasks in this emerging field of machine learning.

• YouTube
• 29 minutes
• Self-Paced
• Free Video

Whisper Paper Explained - Robust Speech Recognition via Large-Scale Weak Supervision

Detailed explanation of OpenAI's Whisper model for speech recognition, covering dataset collection, model architecture, experiments, and scaling considerations.

• YouTube
• 33 minutes
• Self-Paced
• Free Video

Graph Convolutional Network Paper Explained

Comprehensive explanation of Graph Convolutional Networks (GCNs), covering theory, derivation, and practical applications in node classification, with insights into their functionality and effectiveness.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

Latent Dirichlet Allocation with Gibbs Sampling Explained

Comprehensive explanation of Latent Dirichlet Allocation (LDA) with Gibbs Sampling, covering topic modeling, posterior inference, and implementation details. Ideal for those interested in understanding this classic machine learning technique.

• YouTube
• 33 minutes
• Self-Paced
• Free Video

Variational Autoencoder From Scratch in PyTorch

Learn to implement a Variational Autoencoder from scratch using PyTorch, covering architecture, training loop, and inference with practical examples and code demonstrations.

• YouTube
• 40 minutes
• Self-Paced
• Free Video

How to Get Started with AI Art Using MidJourney - Prompt Engineering and Tips & Tricks

Explore AI art creation with MidJourney through prompt engineering techniques, resource recommendations, and practical tips for generating stunning visual results.

• YouTube
• 34 minutes
• Self-Paced
• Free Video

SRGAN Implementation From Scratch

Learn to implement SRGAN from scratch, covering model architecture, VGG loss term, and essential components for super-resolution image enhancement.

• YouTube
• 37 minutes
• Self-Paced
• Free Video

Detecting Facial Keypoints with Deep Learning

Learn to implement a top-performing deep learning solution for facial keypoint detection, covering data analysis, model development, and submission strategies for Kaggle competitions.

• YouTube
• 37 minutes
• Self-Paced
• Free Video

Detecting Eye Disease Using Deep Learning - Kaggle Top 1% Solution, No Ensemble

Learn to detect eye disease using deep learning, with a top-ranked Kaggle solution. Covers data preprocessing, loss functions, augmentation, and resolution techniques for improved accuracy in medical image analysis.

• YouTube
• 59 minutes
• Self-Paced
• Free Video

ProGAN Implementation From Scratch

Learn to implement ProGAN from scratch, covering model architecture, training setup, and evaluation. Gain hands-on experience in advanced generative adversarial network techniques.

• YouTube
• 1 hour 29 minutes
• Self-Paced
• Free Video

ProGAN Paper Walkthrough

Detailed walkthrough of the ProGAN paper, covering progressive growing, MiniBatch Std, layer fading, normalization techniques, and implementation details for advanced GAN enthusiasts.

• YouTube
• 32 minutes
• Self-Paced
• Free Video

CycleGAN Implementation From Scratch

Learn to implement CycleGAN from scratch, covering discriminator, generator, dataset preparation, and training process for image-to-image translation tasks.

• YouTube
• 58 minutes
• Self-Paced
• Free Video

EfficientNet From Scratch in Pytorch

Learn to implement EfficientNet from scratch using PyTorch, covering key concepts like CNNBlock, SqueezeExcitation, and InvertedResidualBlock with stochastic depth.

• YouTube
• 39 minutes
• Self-Paced
• Free Video

How to Solve Santander Kaggle Transaction Competition - Top 1% Solution, No Ensemble

Learn to achieve top 1% in Santander Kaggle competition using neural networks, feature engineering, and data analysis techniques. Improve your machine learning skills with practical insights and strategies.

• YouTube
• 58 minutes
• Self-Paced
• Free Video