BYOB - Build Your Own Bot
Learn to build conversational agents with frameworks, NLP techniques, and best practices. Explore intent recognition, dialogue management, and generative models while avoiding common pitfalls in chatbot development.
Understanding BigBird - Transformers for Longer Sequences
Explore BigBird's block sparse attention for long sequences, comparing it with BERT and Longformer. Learn implementation, complexity, use cases, and performance optimization tips.
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.
Natural Language Processing - Tokenization
Learn the fundamentals of tokenization in NLP, covering simple techniques and NLTK implementation. Gain practical coding skills for text processing and analysis.
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.
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.
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.
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.
Spark NLP - State-of-the-Art Natural Language Processing at Scale
Discover scalable NLP techniques using Apache Spark, covering core algorithms, BERT embeddings, and multi-lingual challenges. Learn to build efficient pipelines for various NLP tasks through practical demonstrations.
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.
Scalable AutoML for Time Series Forecasting Using Ray
Explore scalable AutoML for time series forecasting using Ray, covering feature generation, model selection, and hyper-parameter tuning. Learn from real-world applications and experiences in various industries.
Benchmarks and How-Tos for Convolutional Neural Networks on HorovodRunner-Enabled Apache Spark Clusters
Explore benchmarks and implementation techniques for distributed deep learning using HorovodRunner on Apache Spark clusters, focusing on CNN tasks and scaling efficiency for both GPU and CPU clusters.
Apache Spark NLP for Healthcare - Building Real-World AI Systems
Explore real-world healthcare AI systems using NLP, covering patient risk prediction, diagnosis automation, and revenue optimization. Learn key considerations for building domain-specific models and scalable ML pipelines.
Building a Pipeline for State-of-the-Art NLP Using Hugging Face Tools
Learn to build state-of-the-art NLP pipelines using Hugging Face tools. Explore tokenization, transformer networks, and various NLP tasks like sentiment analysis and question answering.