Exploring and Exploiting Interpretable Semantics in GANs - CVPR 2020 Tutorial
Explore interpretable semantics in GANs, covering deep generative representation, GAN dissection, latent space manipulation, and applications in face synthesis and image processing.
Interpretable Representation Learning for Visual Intelligence
Explore interpretable representation learning in deep neural networks for visual intelligence, covering object classification, scene recognition, and network visualization techniques.
Deep Learning to Discover Coordinates for Dynamics - Autoencoders & Physics Informed Machine Learning
Explore deep learning techniques for discovering effective coordinate systems in dynamical systems, enabling simpler models and physical law discovery. Focus on autoencoders and physics-informed machine learning.
Interpretable Deep Learning for New Physics Discovery
Explore a novel method combining deep learning and symbolic regression to uncover physical laws from data, with applications in physics and cosmology.
Deep Learning for Turbulence Closure Modeling
Explore how deep learning revolutionizes turbulence modeling in fluid dynamics, focusing on RANS equations and large eddy simulations for advanced computational techniques.
Deep Learning of Hierarchical Multiscale Differential Equation Time Steppers
Learn to integrate multiscale differential equations efficiently using a novel deep learning architecture, with benchmarks on illustrative dynamical systems.
Machine Learning for Fluid Dynamics - Patterns
Explore machine learning applications in fluid dynamics, focusing on pattern extraction and coherent structure identification in high-dimensional systems.
Machine Learning for Fluid Mechanics
Overview of machine learning applications in fluid mechanics, exploring historical context, pattern recognition, reduced-order modeling, and flow control, with insights on AI's impact on this data-rich field.
Deep Learning of Dynamics and Coordinates with SINDy Autoencoders
Discover effective coordinate systems and models simultaneously using SINDy autoencoders, a novel approach for analyzing high-dimensional data in dynamical systems.
Long Short-Term Memory with PyTorch + Lightning
Learn to implement and train LSTM networks using PyTorch and Lightning, covering custom LSTM coding, PyTorch's built-in LSTM, and advanced training techniques for deep learning projects.
Introduction to Coding Neural Networks with PyTorch and Lightning
Aprenda a codificar redes neurais eficientemente com PyTorch e Lightning, simplificando o processo, melhorando a portabilidade e otimizando a taxa de aprendizagem automaticamente.
Introduction to PyTorch
Learn to create, visualize, and optimize a neural network using PyTorch in this step-by-step tutorial, covering network creation, output graphing, and parameter optimization through backpropagation.
Create Artificial Neural Network Using Weight Initialization Tricks
Learn weight initialization techniques for creating artificial neural networks, enhancing model performance and convergence in deep learning applications.
Epic Web UI DreamBooth Update - New Best Settings - Stable Diffusion Training Compared on RunPods
Comprehensive guide to optimizing DreamBooth settings in Stable Diffusion, comparing 10 training methods on RunPods for improved AI image generation results.
Neural Style Transfer - From Theory to Pytorch Implementation
Explore neural style transfer using PyTorch, from theory to implementation. Learn to manipulate photos with deep neural networks, creating stunning AI-generated artwork.
Alexnet Architecture In-Depth Discussion Along With Code - Deep Learning Advanced CNN
In-depth exploration of AlexNet architecture, including mathematical operations, layers, and implementation code for advanced deep learning and convolutional neural networks.
Neural Nets for NLP 2019 - Attention
Explore attention mechanisms in neural networks for NLP, covering basic concepts, improvements, specialized varieties, and a case study on the Transformer model.
Neural Nets for NLP - Latent Variable Models
Explore latent variable models in NLP, covering variational autoencoders, discrete variables, and practical applications. Gain insights into generative modeling and advanced neural network techniques.
Neural Nets for NLP 2017 - Attention
Comprehensive exploration of attention mechanisms in neural networks for NLP, covering various types, improvements, and applications, with a focus on the Transformer model.
Neural Nets for NLP 2017 - Recurrent Neural Networks
Comprehensive lecture on recurrent neural networks, covering their architecture, challenges, and applications in NLP, with emphasis on LSTMs and practical implementation techniques.
Neural Nets for NLP 2017 - Convolutional Networks for Text
Comprehensive lecture on convolutional networks for text, covering bag-of-words, n-grams, applications, stacked and dilated convolutions, structured convolution, sentence pair modeling, and CNN visualization techniques.