Unsupervised Learning with Autoencoders
Explore autoencoder variants, their applications in unsupervised learning, and real-world use cases. Learn about anomaly detection, denoising, and generative models in this comprehensive overview.
From Paper to Product - How We Implemented BERT
Explore the journey of implementing BERT in a natural language generation product, from initial research to production deployment. Learn about challenges, successes, and valuable lessons for NLP practitioners.
Predicting the Future as a Service with Azure ML and R
Learn to create predictive analytics using Azure Machine Learning Studio. Explore data manipulation, R modules, publishing endpoints, and model retraining for easy, low-cost machine learning implementation.
SW Testing - Can ML Save Us?
Explore the potential of machine learning in software testing, addressing challenges in GUI testing and discussing promising applications like supervised learning and reinforcement learning.
The Shape of Data - Machine Learning and Topology
Explore unsupervised learning and topological data analysis to uncover hidden insights in complex datasets, with applications in health and finance using Python and R.
Machine Learning - The Bare Math Behind Libraries
Explore the mathematical foundations of machine learning, from supervised to unsupervised learning techniques, to gain a deeper understanding of neural networks and their underlying processes.
Thinking of Topic Modeling as Search in Real-time Applications
Explore how treating topic modeling as a search problem can enhance real-time applications and overcome common challenges in document classification and clustering.
AI-Based Wafer Defect Detection Using Anomaly Detection - Real-Time Implementation
Discover advanced AI techniques for real-time wafer defect detection through unsupervised and semi-supervised learning, focusing on anomaly detection and segmentation in industrial manufacturing environments.
Single Cell Data Integration Using Optimal Transport - 2023
Explore single-cell data integration using optimal transport. Learn unsupervised algorithms for multi-omic studies, alignment techniques, and performance quantification in computational genomics.
Continuous Kendall Shape Variational Autoencoders
Explore unsupervised learning of geometric representations using equivariant VAEs with hyperspherical latents, extending Kendall-shape VAEs for improved representation capacity.
How to Create ML Models That Teach Themselves - Unsupervised Learning in Self-Driving Cars
Explore self-driving car technology and unsupervised ML models with Helm.ai. Learn about deep teaching, real-world applications, and future enhancements in autonomous vehicle development.
Deepnets for Stereo and Structure from Motion - Part 2
Explore semi-supervised depth prediction, wide context learning networks, and unsupervised monocular depth estimation techniques for advanced computer vision applications.
Introduction to Deep Learning - Traditional Approaches vs Deep Learning - Lecture 2
Explore deep learning fundamentals, comparing traditional and modern approaches in machine learning and NLP.
K-Means Implementation in R
Implement K-means clustering algorithm in R programming language. Learn data preprocessing, algorithm application, and result interpretation for effective data analysis.
UnSAM - Unsupervised Segmentation Anything Model: A New Approach to Image Segmentation
Discover UnSAM: a groundbreaking unsupervised image segmentation model. Learn its divide-and-conquer approach, hierarchical structure discovery, and impressive performance across datasets.
Unlocking Natural Artificial Intelligence - Bridging AI and Natural Skill Acquisition
Explore natural AI's potential beyond language models. Discover efficient skill learning through environmental interactions and the Free Energy Principle's role in brain computation.
Embeddings vs Fine-Tuning - Part 3: Unsupervised Fine-Tuning
Explore unsupervised fine-tuning techniques for embeddings, including dataset cleanup, Google Colab implementation, and expert tips for optimal results.
Top Ten Tips for Fine-tuning Large Language Models
Discover ten essential tips for effective fine-tuning of language models, from starting small to advanced techniques like LoRA and preference tuning. Gain practical insights for optimal results.
Learning about Machine Learning
Explore machine learning fundamentals with expert Roger D. Amos in this concise 25-minute lecture from QCHEM LAB at KNU.
Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective - UAI 2023 Oral Session 4
Explore theoretical analysis of learning from low-rank tensor data in supervised and unsupervised settings, quantifying performance gains of tensor methods over standard vector approaches.
Local Message Passing on Frustrated Systems - UAI Oral Session 6
Explore an alternative message passing algorithm for cyclic graphs, challenging SPA's extrinsic principle and optimizing factor node updates for improved performance on complex networks.