Data Mining for Security Anomalies
Explore techniques for collecting, processing, and mining security event data to detect anomalies and generate actionable insights for improved system protection.
Brave New World of Artificial Intelligence
Explore the future of AI, from its foundations to modern applications. Learn about machine learning, knowledge engineering, and real-world AI scenarios across various industries.
Hands-On Regular Expressions in Python
Learn to create, understand, and effectively use regular expressions in Python for data validation, parsing, and normalization. Discover when and how to apply regex for optimal results.
Productionize Research Oriented Code by Python
Discover strategies to transform research-oriented Python code into production-ready applications, focusing on modularization, refactoring, and best practices for robust, maintainable software.
When to Use Machine Learning - Tips, Tricks and Warnings
Discover when and how to effectively apply machine learning, with practical tips and real-world examples from open-source projects and cryptocurrency trading.
How to Train an Image Classifier Using PyTorch
Learn to build an image classifier using PyTorch in this hands-on tutorial. Covers data preparation, model selection, training techniques, and evaluation, with practical tips and code examples for immediate implementation.
Train, Serve, Deploy - Story of a NLP Model ft. PyTorch, Docker, Uwsgi and Nginx
Learn to build, train, and deploy NLP translation models using PyTorch, Flask, Docker, uWSGI, and Nginx. Covers data preprocessing, model training, web app creation, containerization, and production serving techniques.
Protect Your Data by Design - Creating a Data Masking Strategy
Learn to create a comprehensive data masking strategy to protect sensitive information, overcome implementation challenges, and ensure data security in pre-production environments.
Microsoft SQL Server Machine Learning Workloads on Red Hat Enterprise Linux and Red Hat OpenShift
Accelerate machine learning pipelines using SQL Server on Red Hat Enterprise Linux and OpenShift. Learn in-database ML techniques and containerization for efficient model production and deployment.
From Zero to Deep Learning Enhanced App
Practical insights on developing a Deep Learning Android app, from data collection to production deployment using TensorFlow, with tips on choosing the right approach and best practices.
Enterprise Tensorflow
Explore integrating TensorFlow models into Java enterprise environments, covering deployment, version control, and integration with popular server frameworks like Spring and Apache CXF.
Intro to Falco - Intrusion Detection for Containers
Learn to deploy Falco for container intrusion detection, modify rules, and manage alerts. Discover real-world use cases and best practices from Shopify's experience in securing high-volume cloud environments.
How GZIP Compression Works
Explore GZIP compression's internals, comparing implementations and optimizing techniques for better data compression in web development, especially for mobile and low-bandwidth scenarios.
The F#orce Awakens
Explore Star Wars data using F# and R to uncover character importance and analyze movie success, while learning data science techniques applicable to various datasets.
Table Question-Answering with TAPAS in Python
Learn to implement table question-answering using TAPAS in Python, covering retrieval pipelines, dataset preprocessing, and advanced aggregation queries with Hugging Face transformers and Pinecone vector database.
Text Prediction Using LSTM
Learn to train LSTM networks for English text prediction, covering data preparation, model architecture, and character-level forecasting techniques.
Use Case in Demand Forecasting at Fortenova Group DataLab
Discover how DataLab implements demand forecasting at Konzum and Mercator, from cloud data migration to modeling and industrialization, while exploring the evolution of this data analytics center of excellence.
Smart Approach in Development and Deployment Process for ML Models
Discover efficient strategies for ML model development, focusing on smart data mart creation, preprocessing techniques, and streamlined deployment processes using Python/PySpark.
A Type System for Feature Engineering in Machine Learning
Explore a dependent type system for feature engineering, enhancing machine learning model inputs through advanced data operations and compiler techniques.
How Well Do LLMs Detect Anomalies in Your Data? - Conf42 LLMs 2024
Explore LLMs' effectiveness in detecting data anomalies, understand data quality importance, and learn practical techniques for anomaly detection using OpenAI and BigQuery.
Training and Using a Large Language Model to Write Theatrical Performances
Explore AI-assisted creative writing for theater, covering LLM training, data preprocessing, and collaborative script generation. Learn about challenges and techniques in NLP for theatrical performances.