Explains the difference between .update() and .forward() methods in the TorchMetrics library for evaluating PyTorch models.
Explores how Large Language Models perform implicit Bayesian inference through in-context learning, connecting exchangeable sequence models to prompt-based learning.
Announcing a new book on machine learning, covering fundamentals with scikit-learn and deep learning with PyTorch, including neural networks from scratch.
Author announces a new machine learning book covering scikit-learn, deep learning with PyTorch, neural networks, and reinforcement learning.
Explores active learning strategies for selecting the most valuable data to label when working with a limited labeling budget in machine learning.
Practical strategies for staying current in the fast-moving field of machine learning, including project experimentation and community engagement.
A tutorial on fine-tuning a Vision Transformer (ViT) model for satellite image classification using Hugging Face Transformers and Keras.
A comprehensive list of 90 machine learning lecture videos covering topics from Python basics to advanced ML concepts like decision trees and Bayesian methods.
A comprehensive collection of 90 machine learning lecture videos covering Python, scikit-learn, algorithms, and model evaluation techniques.
A summary of key papers and talks from the RecSys 2021 conference, focusing on collaborative filtering, model comparisons, and deployment strategies.
A forecast of speech recognition technology's evolution from 2010 to 2030, analyzing past progress and predicting future trends.
An in-depth technical explanation of diffusion models, a class of generative AI models that create data by reversing a noise-adding process.
A comprehensive deep learning course covering fundamentals, neural networks, computer vision, and generative models using PyTorch.
A comprehensive deep learning course overview with PyTorch tutorials, covering fundamentals, neural networks, and advanced topics like CNNs and GANs.
Explores how images are discretized into pixels, the impact of sampling grids on deep learning models, and inconsistencies in image processing libraries.
Explains contrastive representation learning, its objectives like contrastive and triplet loss, and its use in supervised and unsupervised machine learning.
Explores how Stochastic Gradient Descent (SGD) inherently prefers certain minima, leading to better generalization in deep learning, beyond classical theory.
Exploring how deep learning and a pre-trained geolocation model can be used to automate and improve performance in the GeoGuessr geographic discovery game.
A technical exploration of the β-VAE objective from an information maximization perspective, discussing its role in learning disentangled representations.
A curated list of public dataset repositories for machine learning and deep learning projects, including computer vision and NLP datasets.
