Resolution-Robust Large Mask Inpainting with Fourier Convolutions - With Author Interview
Explore LaMa, an advanced image inpainting system using Fourier convolutions for large mask removal. Learn about its architecture, loss function, and impressive results in reconstructing complex structures.
PonderNet - Learning to Ponder - Machine Learning Research Paper Explained
Explore DeepMind's PonderNet, a novel approach to adaptive computation in neural networks. Learn how it dynamically allocates computational steps based on problem complexity, improving efficiency and performance.
Build a Neural Network with Python Tutorial - Deep Learning with PyTorch
Learn to build, train, and evaluate a neural network for weather prediction using PyTorch. Covers data preprocessing, model architecture, GPU acceleration, and practical applications.
Build a Deep Facial Recognition App - Training a Siamese Neural Network - Python
Learn to train a Siamese Neural Network for facial recognition using Python and TensorFlow. Covers loss functions, optimizers, custom training steps, and model training implementation.
Advance Deep Learning Workshop for Satellite Imagery - Model Export & Production
Hands-on workshop on advanced deep learning techniques for satellite imagery, covering remote debugging, model deployment, activation heatmaps, and custom predictions using TensorFlow and Keras.
Gradient Descent, Step-by-Step
Comprehensive step-by-step explanation of Gradient Descent, covering its application in Machine Learning, optimization techniques, and variations like Stochastic Gradient Descent.
Neural Nets for NLP - Debugging Neural Nets for NLP
Practical strategies for debugging and optimizing neural networks in NLP, covering issues like training, overfitting, initialization, and evaluation metrics.
Neural Nets for NLP 2017 - A Simple Exercise - Predicting the Next Word in a Sentence
Explore word prediction techniques, including skip-grams, CBOW, and advanced word vector methods. Learn to describe, count, predict, evaluate, and visualize word relationships in natural language processing.
CMU Advanced NLP 2021 - Adversarial Learning
Explore adversarial learning in NLP, covering GANs, discrete outputs, and input adversaries. Gain insights into advanced techniques for robust language models and style transfer.
Regularized Least Squares
Explore statistical learning theory with Lorenzo Rosasco, focusing on regularized least squares, optimization techniques, and key concepts in machine learning algorithms.
Is the Problem in the Data? Examples on Fairness, Diversity, and Bias
Exploring AI bias through interactive examples on fairness, diversity, and hidden biases. Examines whether bias issues stem from data or other factors in machine learning models.
Supervised Contrastive Learning
Explore a novel supervised learning approach that outperforms cross-entropy loss, improving image classification models' performance and robustness across various architectures and data augmentations.
Latent Variable EBMs for Structured Prediction
Explore advanced machine learning concepts including energy-based models, contrastive methods, latent variable models, and structured prediction. Gain insights into cutting-edge techniques and algorithms in deep learning.
Joint Embedding Method and Latent Variable Energy Based Models
Explore joint embedding methods and latent variable energy-based models with Yann LeCun. Learn about predictive systems, inference, probabilistic models, and various training techniques for advanced machine learning applications.
Gradient Descent and the Backpropagation Algorithm
Explore gradient descent and backpropagation algorithms, covering supervised learning, parametrized models, loss functions, and neural network architectures with PyTorch implementation.
Tools, Classification with Neural Nets, PyTorch Implementation
Explore neural network classification, PyTorch implementation, and advanced concepts like space-fabric stretching and regression uncertainty estimation in this comprehensive tutorial.
Structured Prediction with Energy Based Models
Explore energy-based models for structured prediction, covering factor graphs, efficient inference, Graph Transformer Networks, loss functions, and variational inference in deep learning applications.
PyTorch Activation and Loss Functions
Explore activation and loss functions in PyTorch, including kink vs. smooth activations, margin-based losses, and designing effective loss functions for Energy-Based Models (EBMs).
Stochastic Gradient Descent and Backpropagation
Explore stochastic gradient descent, backpropagation, and neural network implementation in PyTorch. Learn practical tricks, basic modules, and gradient computation for effective deep learning.
CNN Loss
Explore convolutional neural network loss functions, including mean square error, cross-entropy, and negative log likelihood, with practical examples and implementation insights.
MIT Introduction to Deep Learning
Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques.