Neural Network Programming - Deep Learning with PyTorch
Learn to build and train neural networks using PyTorch, covering tensor operations, CNN architecture, data preparation, training loops, and performance optimization techniques for deep learning projects.
Federated Hyperparameter Tuning - Challenges, Baselines, and Connections
Explores challenges and solutions for hyperparameter tuning in federated learning, introducing FedEx method to accelerate the process. Connects to weight-sharing in neural architecture search and demonstrates improved accuracy on benchmarks.
Gradient Boost
Explore the fundamentals and advanced concepts of Gradient Boost for regression and classification in machine learning.
Tabular Data
Learn machine learning techniques for tabular data, including model evaluation, feature engineering, tree-based models, hyperparameter tuning, and neural networks using AWS SageMaker.
Using Reproducible Experiments to Create Better Machine Learning Models
Enhance ML model efficiency through reproducible experiments. Learn to track hyperparameter, code, and data changes using DVC for grid and random search tuning methods.
Automated Machine Learning Tuning with FLAML
Explore automated machine learning and tuning using FLAML, a fast Python library. Learn to automate ML tasks, perform general tuning, and conduct zero-shot AutoML efficiently.
Complete Deep Learning
Comprehensive deep learning course covering neural networks, CNN, RNN, LSTM, and advanced topics with hands-on tutorials and practical implementations.
Python Data Science Project
Learn Python for data science through hands-on projects covering machine learning, web scraping, exploratory data analysis, and computational drug discovery. Gain practical skills using popular libraries and tools.
Tensor2Tensor - TensorFlow at O’Reilly AI Conference, San Francisco '18
Explore state-of-the-art deep learning models and datasets with Tensor2Tensor, accelerating ML applications like translation, parsing, and image captioning.
MLOps: Databricks MLFlow y Optuna para Ajuste de Hiperparámetros - Español
Optimiza modelos de machine learning con Databricks, MLflow y Optuna. Aprende técnicas de ajuste de hiperparámetros usando XGBoost en un entorno práctico.
MLOps MLFlow: Databricks y MLFlow Hyper-Parameter Tuning en Español
Optimiza modelos de machine learning con Databricks y MLflow, utilizando Hyperopt y XGBoost para ajuste de hiperparámetros.
Amber - A Complete, ML-Based, Anomaly Detection Pipeline for Microcontrollers
Discover a complete ML-based anomaly detection pipeline for microcontrollers, featuring real-time sensor value processing, automatic hyperparameter tuning, and high-speed inference capabilities.
Statistical Rethinking 2023 - Multilevel Models
Explore multilevel models, partial pooling, and hyperparameter tuning through practical examples like reedfrogs. Learn advanced techniques for statistical analysis and model optimization.
SVM Kernels In-Depth Intuition - Polynomial Kernels Part 3 - Machine Learning Data Science
Explore polynomial kernels in SVM, understanding their intuition, use cases, and hyperparameter tuning for enhanced machine learning model performance.
Intro to Machine Learning with R & Caret
Learn to leverage R's caret package for efficient machine learning tasks, including data sampling, model training, cross-validation, and hyperparameter tuning. Enhance your data science skills with practical examples.
Step-by-Step Machine Learning Project Implementation: EDA, Feature Engineering, Selection, and Model Training
Comprehensive guide to implementing a machine learning project, covering data cleaning, EDA, feature engineering, selection, model training, and hyperparameter tuning.
Introduction to Decision Trees - Gini Impurity and Python Code
Learn decision tree fundamentals, including Gini impurity and Python implementation, through a beginner-friendly introduction with concrete examples and code for sepsis survival prediction.
Machine Learning Simplified - From Ideation to Deployment in Minutes with Automated ML
Discover how to train high-quality ML models using Azure Machine Learning's automated ML, requiring only a dataset and minimal configuration parameters.
A Cabinet of Deep Learning Curiosities - Unconventional Techniques and Overlooked Methods
Explore unconventional deep learning techniques, from obscure hacks to overlooked best practices, that have proven surprisingly effective in real-world projects.
From Python to PySpark and Back Again - Unifying Single-Host and Distributed Deep Learning with Maggy
Unifying single-host and distributed deep learning with Maggy framework, enabling seamless transition from laptop to cluster for efficient model development and training using TensorFlow and PySpark.
Revisiting Knowledge Graph Completion From a Practical Perspective - Danai Koutra
Explore practical aspects of knowledge graph completion, covering construction, calibration, open-world assumptions, and human-AI collaboration for improved link prediction and triple classification.