Machine Learning at Amazon by Rajeev Rastogi
Explore machine learning applications at Amazon, from product recommendations to size prediction, with insights on challenges and innovative approaches in e-commerce AI.
ProtoDash - Fast Interpretable Prototype Selection by Karthik Gurumoorthy
Explore ProtoDash, a fast interpretable prototype selection method for extracting compact representations of large datasets, with applications in causal reasoning, data relationships, and prediction accuracy improvement.
MD Simulations and Machine Learning to Quantify Interfacial Hydrophobicity
Explore molecular dynamics simulations and machine learning techniques to analyze interfacial hydrophobicity, focusing on efficient data analysis methods and topological approaches for complex chemical systems.
Gunnar Carlsson - Deep Learning and TDA
Explore deep learning's interaction with Topological Data Analysis, focusing on explainability and performance enhancement in neural networks and various data analysis applications.
Intelligence in Marketing - Real or Artificial?
Explore AI and Customer Intelligence for enhanced customer engagement and discover the future of marketing. Learn about market orientation, qualitative data, and the impact of COVID-19 on marketing strategies.
Data Mesh and Microservices: Leveraging Trusted Data Events with GoldenGate
Explore Data Mesh principles, microservices architecture, and GoldenGate's role in creating trusted data events for improved business outcomes and distributed data management.
Intelligent Model Deployment and Consumer Pipeline With DataRobot AI Cloud Platform
Explore DataRobot AI Cloud's MLOps capabilities, including model deployment, registry, and inference. Learn to deploy models from leaderboards and consume them via API, stream, local mode, and job workflows.
Always Know What to Expect From Your Data With Great Expectations
Learn to validate, test, and document data quality using great_expectations. Covers installation, initialization, context, demos with various datasets, and practical applications for data teams.
Technical Overview of Machine Learning Life Cycle
Comprehensive technical overview of the machine learning lifecycle, covering data preparation, model training, deployment, and inference. Ideal for AI engineers seeking in-depth understanding of ML processes.
An Improved and Functional Design of End-to-End Machine Learning Life Cycle
Comprehensive overview of the machine learning lifecycle, covering data preparation, model training, deployment, and inference, with practical insights for effective implementation and monitoring.
An AI Engineer's Guide to Machine Learning with Keras
Comprehensive guide to building and evaluating machine learning models using Keras, covering data preparation, model creation, training, and performance analysis with practical examples.
Build Your Machine Learning Pipeline in These 4 Simple Steps - Loan Prediction Scikit-Learn Example
Aprenda a construir um pipeline completo de machine learning usando scikit-learn para previsão de empréstimos, desde a ingestão de dados até o uso do modelo salvo para previsões.
Access 2019 Beginner Tutorial
Comprehensive tutorial on Microsoft Access 2019 for beginners, covering database creation, table design, data import, relationships, queries, forms, and reports. Includes hands-on exercises and advanced topics.
Fine-Tuning Alpaca- Train Alpaca LoRa for Sentiment Analysis on a Custom Dataset
Learn to fine-tune Llama 7B with Alpaca LoRa on a custom bitcoin sentiment dataset. Covers data preprocessing, model training, and performance analysis for sentiment detection in cryptocurrency tweets.
Generative Python Transformer Model Released - The Off-Brand Github Copilot
Explore GPyT, an open-source alternative to Github Copilot. Learn how to use this generative Python transformer model for code autocompletion and generation across various programming tasks.
How Can Neural Networks Identify Fake Face Images?
Explore neural networks' ability to detect fake face images using Grad-CAM algorithm. Learn about datasets, model training, and visualization techniques for understanding AI decision-making in image classification.
Install Depth Model in Stable Diffusion 2.0
Learn to integrate depth models into Stable Diffusion 2.0, enhancing image generation capabilities with step-by-step guidance on installation and execution.
Hierarchical Clustering on Asymmetric Networks
Explore hierarchical clustering techniques for asymmetric networks, examining methodologies and applications in data analysis and network science.
Dimension Reduction - An Overview
Comprehensive overview of dimension reduction techniques, from linear methods like PCA to nonlinear approaches such as LLE, Isomap, t-SNE, and UMAP, with practical application to chemical reaction data.
On the Topological Expressiveness of Neural Networks
Explore the topological constraints of neural network architectures and their impact on function representation, with insights from recent research and ongoing work in the field.
Skeletonization Algorithms With Theoretical Guarantees for Point Clouds
Explore skeletonization algorithms for point clouds, including Mapper, alpha-Reeb graphs, and Homologically Persistent Skeleton. Compare their performance on synthetic and real data sets.