Kristen Grauman - Learning the Right Thing with Visual Attributes
Explore visual attribute learning techniques, including shared properties, transfer learning, and local approaches for fine-grained attribute differences in computer vision.
DALL-E Mini - Generate Images From a Text Prompt
Learn about DALL·E mini, a smaller version of OpenAI's DALL·E that generates images from text prompts. Explore its architecture, training process, and capabilities in this comprehensive overview.
Captcha Recognition Using PyTorch - Convolutional-RNN + CTC Loss
Learn to implement a Convolutional-RNN model for captcha recognition using PyTorch. Covers data preparation, model architecture, CTC loss, and training/inference processes.
Image Restoration Using Deep Learning - Dehazing
Explore image restoration using deep learning, focusing on dehazing techniques. Learn about neural networks, dynamic units, and advanced methods for enhancing hazy images.
Jeffrey Mark Siskind - Computer Vision Language
Explore the intersection of computer vision and language processing, focusing on video captioning, proposal generation, and unified cost functions.
Detecting Masked Faces in the Pandemic World
Explore deep learning techniques for detecting masked faces during the pandemic with Vladimir Iglovikov, a Sr. Computer Vision Engineer at Lyft and Kaggle Grandmaster.
Build a Web-App to Serve a Deep Learning Model for Skin Cancer Detection
Learn to build a Flask web app that serves a deep learning model for skin cancer detection, covering file uploads, predictions, and UI design with Bootstrap.
Implementing a GAN to Remove People from Images - Databricks Lightning Talk
Learn to implement a pix2pix GAN on Databricks for removing people from images, exploring the basics, idea, demo, and analysis of this innovative image editing technique.
Using Docker Images in Production - Databricks Case Study
Learn how Condé Nast evolved Spire, their user segmentation service, from notebooks to Docker images on Databricks clusters. Explore the streamlined deployment workflow, challenges faced, and solutions implemented.
Consolidating MLOps at Schiphol Airport Using MLFlow
Explore how Schiphol Airport leverages MLFlow for rapid iteration, deployment, and monitoring of mission-critical machine learning models, enabling multiple weekly releases and efficient adaptation to changing dynamics.
How Brain Computations Can Inspire New Paths in AI - Part 2
Explore brain-inspired AI advancements, focusing on object recognition, contextual reasoning, and model performance. Gain insights into cutting-edge computational approaches and their applications.
Question Answering for Language and Vision
Explore advanced techniques in question answering for language and vision, including joint models, multitask learning, and neural networks with Richard Socher from MetaMind.
Neural Network Architectures for Images
Explore neural network architectures for image processing, including classification, segmentation, and enhancement. Learn about CNNs, ResNets, autoencoders, and their applications in various imaging tasks.
Unlearned Neural Networks as Image Priors for Inverse Problems
Explore unlearned neural networks as image priors for inverse problems in imaging, covering Deep Image Prior, Deep Decoder, and Deep Geometric Prior techniques and their applications.
Transfer Learning Using Keras
Learn to classify car brands using transfer learning with Keras and ResNet50. Hands-on tutorial covers dataset preparation, model configuration, training, and prediction visualization.
Transfer Learn in Weather Images
Learn to classify weather images using transfer learning with Python, TensorFlow, and VGG19. Covers dataset preparation, model implementation, and image prediction.
Cancer Detection in Brain Using Deep Learning
Learn to detect brain tumors using deep learning, CNN, and TensorFlow. Build, train, and test a model for classifying brain images with hands-on coding and practical implementation.
How to Classify Flowers Using Deep Learning
Learn to build a convolutional neural network for flower image classification using TensorFlow and Keras, from dataset preparation to model testing and prediction.
Training a GAN from Your Own Images - StyleGAN2 ADA
Learn to create custom GANs using your own images, from setup to training. Covers image collection, Docker setup, TFRecord conversion, and understanding GAN outputs for various projects like FishGAN and MinecraftGAN.
Training a GAN on Your Own Images - StyleGAN2 ADA PyTorch
Learn to install and use NVIDIA StyleGAN2 ADA for PyTorch on Windows 10, including setup, training, and image generation with new Ampere GPUs. Step-by-step guide for both beginners and advanced users.
Cloud-Based Convolution Neural Network Ensembles for Computer Vision Counting
Explore ensemble CNN techniques for computer vision counting, featuring cloud computing evaluation and strategies for achieving low RMSE scores in competitive settings.