SetFit with SBERT for Few-Shot Text Classification - Multi-Class and Multi-Label
Master SetFit and SBERT techniques for efficient few-shot text classification, exploring contrastive training, dense embeddings, and classification head implementation for both multi-class and multi-label scenarios.
SetFit and SBERT: Zero-Shot Classification with Synthetic Data Sets - Part 47
Discover how to leverage SetFit and SBERT for zero-shot classification by creating and utilizing synthetic data, enhancing performance in both zero-shot and few-shot scenarios.
Text Vectorization Made Easy - A Tutorial on Keras Preprocessing Layers for AI
Master text vectorization techniques in Keras preprocessing layers for transforming natural language inputs into numerical sequences and building efficient AI input processing pipelines.
Neural Classification with Word Embeddings - PyTorch Tutorial
Master neural classification techniques and word embeddings implementation in PyTorch through hands-on practice with essential deep learning concepts and practical applications.
Productionize AI and ML Algorithms in Cloud Environments
Explore AI and ML algorithm productionization in cloud environments. Learn development phases, NLP tasks, Hugging Face, MLflow, and AWS deployment for effective model management and serving.
FNet - Mixing Tokens with Fourier Transforms
Explores FNet, a Transformer variant replacing attention with Fourier transforms. Discusses architecture, token mixing, and performance comparisons, highlighting efficiency gains and accuracy trade-offs in natural language processing tasks.
Fake News Classifier with Machine Learning and NLP - Part 1
Learn to build a fake news classifier using machine learning and NLP techniques. Covers data preprocessing, feature extraction, and multinomial classification algorithms for detecting misinformation.
CMU Multilingual NLP 2020 - Advanced Text Classification-Labeling
Explore advanced text classification and sequence labeling methods, including subword models, unsupervised training, and structured prediction for multilingual NLP applications.
CMU Multilingual NLP 2020 - Text Classification and Sequence Labeling
Explore text classification and sequence labeling in multilingual NLP, covering models, datasets, and neural networks for various language processing tasks.
Adversarial Examples in Machine Learning - Crafting and Defending Against Attacks
Explore machine learning vulnerabilities, adversarial examples, and their impact on system security. Learn crafting techniques and defenses through practical tutorials and real-world applications.
Applying Machine Learning Techniques to Detect and Understand Fake News
Explore machine learning techniques for detecting and understanding fake news, covering digital media, web scraping, preprocessing, and classification methods.
Prompt Engineering - Crash Course
Explore prompt engineering techniques to effectively communicate with AI language models, enhancing skills in text summarization, question answering, and classification.
The Fundamentals of Modern Deep Learning with PyTorch
Learn PyTorch for deep learning: train neural networks for image and text classification, explore tensor operations, automatic differentiation, and modern libraries like PyTorch Lightning for efficient GPU training.
Langchain Document Tagging with OpenAI
Create a tool for tagging documents using Langchain and OpenAI, enhancing document organization and retrieval in data science and machine learning projects.
Fine-tuning Flan-T5 for Text Classification with MLFlow
Aprende a personalizar el modelo T5 base para clasificación de secuencias usando tu propio conjunto de datos y MLflow para MLOps.
Natural Language Processing with Deep Learning and TensorFlow
Explore deep learning for NLP using TensorFlow. Learn to create embeddings, build neural networks, and apply them to classification tasks with hands-on labs and interactive learning.
Where AI Is Today and Where It's Going
Explore AI's current capabilities and future potential with Richard Socher, covering image classification, computer vision, text analysis, and the transformative impact of AI on various fields.
Diving into Robocall Content with SnorCall
Explore SnorCall, a framework for analyzing robocall content at scale. Learn about its applications in identifying scam trends, monitoring political calls, and developing better defenses against unsolicited bulk telephone calls.
Repository Data Mining on GitHub
Explore data mining techniques on GitHub repositories, covering machine learning, text classification, and collaborative development practices for enhanced project insights and efficiency.
Deep Learning and Spark
Learn to classify millions of messages using trained deep learning models (fasttext) with Spark. Includes explanations of key concepts and a practical demo on model training and message classification.
Scikit-learn to "Learn Them All"
Overview of Scikit-learn for machine learning in Python, covering supervised and unsupervised techniques, with code examples and insights on implementation. Includes comparison to other ML libraries and scaling to big data.