Innovating Faster with Security and Quality - GitLab and HackerOne Best Practices
Explore GitLab's secure code shipping practices, tools, and industry best practices. Learn how they leverage HackerOne's community for swift security issue detection and prioritization.
Git-Based CI-CD for ML
Learn to implement Git-based MLOps for continuous delivery, automating workflows, and enabling collaboration. Explore open-source tools and hosted ML platforms with a live demo on streamlining ML pipelines.
Choosing Your Giants: Principles for Navigating Emerging Tech Landscape
Principles for evaluating and adopting emerging technologies, balancing business needs with engineer engagement. Explores case studies and research to create a mindful framework for tech choices in software development.
How to Train a Deep Learning Model Using Docker
Learn to train deep learning models using Docker, NVIDIA runtimes, and PyTorch for melanoma classification on a single GPU, with practical code implementation and troubleshooting tips.
End to End ML Project With Deployment - Github and Code Set Up
Comprehensive guide to setting up an end-to-end machine learning project, covering GitHub repository creation, environment setup, and initial code commits.
Photon Vectorized Execution Engine - Technical Deep Dive
Explore Photon's vectorized execution engine, learning about expression evaluation, compute kernels, runtime adaptivity, and vectorized operations for enhanced data processing efficiency.
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.
Architect's Open-Source Guide for Data Mesh Architecture
Explore Data Mesh architecture, its principles, benefits, and implementation challenges. Learn about open-source tools like Apache Spark for building effective Data Mesh systems in real-world scenarios.
Prioritizing Technical Debt Using Version Control Data
Discover data-driven techniques to prioritize technical debt, balance short and long-term goals, and make informed decisions about code improvements using version-control insights from real-world codebases.
Cutting Code Quickly: From 0% to Cleanly Refactored 100% Tested Code
Discover powerful techniques for efficient legacy code refactoring: combination testing, code coverage guidance, and provable refactorings. Learn to quickly achieve 100% test coverage and make impactful changes.
30 Years of Linux and Open Source: Future Trends and Community Sustainability
Fireside chat exploring Linux and Open Source's evolution, future challenges, and opportunities, featuring insights from Nithya Ruff on community sustainability, standards, and diversity in tech.
Setting Up CUDA, CUDNN, Keras, and TensorFlow on Windows 11 for GPU Deep Learning
Comprehensive guide for setting up GPU-accelerated deep learning environment on Windows 11, covering CUDA, CUDNN, Keras, and TensorFlow installation with step-by-step instructions.
Training a GAN on Your Own Images - StyleGAN2 ADA PyTorch
Learn to install and use NVIDIA StyleGAN2 ADA for PyTorch on Windows 10, including setup, training, and image generation with new Ampere GPUs. Step-by-step guide for both beginners and advanced users.
TensorFlow 2.0 GPU, Keras, and Python 3.7 Installation Guide for Windows 10 - 2020 Update
Comprehensive guide for setting up TensorFlow 2.0 with GPU support on Windows 10, covering installation of necessary drivers, CUDA, cuDNN, and Anaconda environment configuration.
Raspberry Pi Pico and RP2040 - The Deep Dive
Dive deep into Raspberry Pi Pico and RP2040 programming, exploring assembly code, compiler optimizations, debugging techniques, and MicroPython for advanced microcontroller development.
Applied Cryptography and Trust - Hashing
Explore hashing methods, digital certificates, password security, and key derivation in this comprehensive overview of cryptographic techniques for data integrity and authentication.
End to End ML Project With Deployment - Project Problem Statement, EDA and Model Training
Comprehensive guide to building and deploying a machine learning project, covering problem definition, exploratory data analysis, feature engineering, and model training.
Parse Developer Q&A: Cloud Code, Push Notifications, and SDK Support
Meta's developer advocates answer questions on Parse features, including Cloud Code, push notifications, data management, and SDK support, offering insights and tips for developers.
Build: Getting Started with Parse - Data Storage, Push Notifications, and Analytics
Learn to build apps quickly with Parse's features like data storage, push notifications, and analytics, focusing on creating unique experiences without server worries.
Building Powerful Mobile Apps with Parse
Learn to build powerful mobile apps using Parse, covering data sharing, login, push notifications, and cloud integration for a comprehensive development experience.
Getting Started with Parse - Cloud-Based Platform for App Development
Learn to build engaging apps with Parse's cloud platform. Explore data APIs, social features, push notifications, and cloud code for efficient app development.