ChatGPT vs Flan-T5: Comparing Proprietary and Free LLMs with Performance Tuning
Explore the differences between ChatGPT and Flan-T5 LLM models, comparing their capabilities, accessibility, and performance optimization through practical demonstrations and hyperparameter tuning techniques.
Troubleshooting Deep Neural Networks - Full Stack Deep Learning - Spring 2021
Learn a systematic approach to troubleshoot deep neural networks, from starting simple to tuning hyperparameters, with practical strategies for implementation, debugging, evaluation, and improvement.
Managing Thousands of Automatic Machine Learning Experiments with Argo and Katib
Explore best practices for managing large-scale AutoML experiments using Argo and Katib in cloud-native environments, focusing on hyperparameter tuning and workflow orchestration.
Improving Stable Diffusion Images with FreeU - Optimizing SDXL, LCM, and Turbo Models
Optimize Stable Diffusion images using FreeU hyperparameters. Learn to leverage ComfyUI API for grid searching ideal parameters in SDXL, LCM, and Turbo models. Practical demonstrations and comparisons included.
Cloud-Based Generative AI Supporting Preliminary Engineering Design
Optimize engineering design with cloud-based generative AI. Learn how Rolls-Royce and Databricks reduced cGAN training time by 30x using distributed computing, MLflow, and Unity Catalog for aerospace compliance.
Open Source Vizier: Distributed Infrastructure for Blackbox Optimization and Hyperparameter Tuning
Explore Google's Vizier system for blackbox optimization and hyperparameter tuning, covering its distributed infrastructure, Python implementation, and integration with AutoML ecosystem.
Gradient Descent: The Ultimate Optimizer - Automating Hyperparameter Optimization
Discover how to automatically optimize hyperparameters in gradient-based ML algorithms through an elegant modification to backpropagation, making optimizer tuning more efficient and less sensitive to initial choices.
Complete TensorBoard Guide
Comprehensive guide to TensorBoard features including loss plots, image visualization, confusion matrices, hyperparameter tuning, embedding projections, and TensorFlow profiler for model optimization and analysis.
Build a Street Fighter AI Model with Python - Gaming Reinforcement Learning Full Course
Learn to create an AI model for Street Fighter using Python, covering setup, preprocessing, hyperparameter tuning, and model fine-tuning. Gain hands-on experience in gaming reinforcement learning.
TorchStudio - Deep Learning Toolkit
Hands-on guide to TorchStudio: Access PyTorch datasets, train models, and compare results. Ideal for beginners and experienced users in the PyTorch ecosystem.
Reinforcement Learning for Gaming - Full Python Course
Comprehensive guide to applying reinforcement learning in gaming using Python and Stable Baselines 3, covering Mario, Doom, Street Fighter, and Chrome Dino with practical missions and best practices.
Subword Features in Machine Learning for Text
Explore machine learning techniques for text analysis using tidymodels to predict Hawaii post offices from their names, with practical code examples and performance evaluation.
Tuning XGBoost Using Tidymodels
Optimize XGBoost models for beach volleyball match prediction using tidymodels, exploring data, tuning hyperparameters, and interpreting results.
Tuning Random Forest Hyperparameters with Tidymodels
Learn to optimize random forest models using tidymodels, exploring hyperparameter tuning techniques with San Francisco tree data for improved predictive performance.
Hyperparameter Tuning Using Tidymodels
Explore hyperparameter tuning for random forest models using global food consumption data, enhancing model performance and gaining insights into international dietary patterns.
Building and Deploying a Model Using AutoML in Azure ML - Azure Tutorial for Beginners
Learn to streamline machine learning model development with Azure AutoML. Explore algorithm selection, hyperparameter tuning, and evaluation metrics for efficient model creation and deployment.
Scalable AutoML for Time Series Forecasting Using Ray
Explore scalable AutoML for time series forecasting using Ray, covering feature generation, model selection, and hyper-parameter tuning. Learn from real-world applications and experiences in various industries.
Tuning Machine Learning Models - Scaling, Workflows, and Architecture
Automating and scaling ML model tuning using Hyperopt and Apache Spark. Best practices for workflows, architecture, and optimization in hyperparameter tuning for improved performance and accuracy.
Productionizing Deep Reinforcement Learning with Spark and MLflow
Aprenda a aplicar Deep Reinforcement Learning em escala usando Spark e MLflow para personalização de jogos e aumento de engajamento, com dicas práticas para superar desafios de produção.
Efficient Distributed Hyperparameter Tuning with Apache Spark
Learn to efficiently distribute hyperparameter tuning workloads using Apache Spark, avoiding common pitfalls and optimizing performance. Explore best practices with Hyperopt and joblib-spark for scaling ML model training.
Building AutoML Pipelines with Argo Workflows and Katib
Explore AutoML pipelines using Argo Workflows and Katib for efficient model selection and hyperparameter tuning in Kubernetes environments, enhancing MLOps processes.