What Kind of AI Can Help Manufacturing Adapt to a Pandemic
Explore how AI, particularly DDNs and L-DNNs, can revolutionize quality inspections in manufacturing, adapting to challenges like COVID-19 and Industry 4.0 for improved efficiency and product quality.
Dataset Management for Computer Vision - Important Component to Delivering Computer Vision Solutions
Explore dataset management's crucial role in computer vision solutions. Learn to align business goals, express assumptions, and maintain datasets for successful real-world implementations.
Relationships Matter - Using Connected Data for Better Machine Learning
Explore graph-native ML for enhanced predictions using relationship data. Learn graph feature engineering, ML techniques, and hands-on applications to leverage network structures in your data science models.
Learning to Optimize High Dimensional Optimization Problems
Explore deep neural networks and reinforcement learning for solving high-dimensional optimization problems, improving performance in job scheduling, neural architecture search, and black-box optimization.
Information Flow and Deep Representation Learning
Explore information bottleneck analysis in deep learning, gaining insights into representation learning, nuisance insensitivity, and disentanglement for advanced understanding of neural networks.
An Introduction to Transfer Learning in NLP and HuggingFace
Explore breakthroughs in NLP through transfer learning and Transformers. Discover HuggingFace's open-source tools for democratizing NLP research and development.
Automated Hyperparameter Tuning for Deep Neural Networks
Comprehensive guide to automated hyperparameter tuning for deep neural networks using Optuna, covering dataset preparation, model design, and optimization techniques in PyTorch.
What Are CycleGANs? - A Novel Deep Learning Tool in Pathology
Explore CycleGANs for unpaired image conversion, including applications in pathology and microscopy. Learn model architecture, training, and inference through code demos and real-world examples.
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.
How to Convert Almost Any PyTorch Model to ONNX and Serve It Using Flask
Learn to convert PyTorch models to ONNX format and deploy them using Flask, focusing on a BERT sentiment model. Gain practical skills in model conversion and API development.
How to Train a Deep Learning Model Using Docker
Learn to train deep learning models using Docker, NVIDIA runtimes, and PyTorch for melanoma classification on a single GPU, with practical code implementation and troubleshooting tips.
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.
Deep Learning for Survival Analysis
Explore deep learning models for survival analysis, applying them to predict time-to-event outcomes in various fields like credit, machinery, and healthcare with Louise Ferbach.
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.
Building an Entity Extraction Model Using BERT
Learn to build an entity extraction model using BERT, transformers library, and PyTorch. Covers dataset preparation, configuration, training, and prediction for creating custom entity extraction solutions.
Dockerizing the Skin Cancer Detection Web Application
Learn to containerize a skin cancer detection web app using Docker, enhancing deployment and scalability of machine learning projects.
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.
Introduction to T5 for Sentiment Span Extraction
Explore T5's text-to-text approach for NLP tasks, focusing on sentiment span extraction in tweets. Learn about multi-task learning and its potential impact on industrial NLP applications.
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.
Taking Machine Learning to Production with MLflow - New Features and Best Practices
Explore MLflow's latest features for productionizing machine learning, including model management, CI/CD, data schemas, and integration with PyTorch for seamless deployment and operation of ML applications.
Taking Deep Learning to Production with MLflow and RedisAI
Explore MLflow and RedisAI integration for efficient deep learning model deployment, featuring multi-framework support, auto-batching, and DAGing capabilities in a reliable runtime environment.