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Learn Hyperparameter Tuning, earn certificates with paid and free online courses from Stanford, IIM Bangalore, DeepLearning.AI and other top universities around the world. Read reviews to decide if a class is right for you.
Learn industry-standard techniques for hyperparameter tuning in machine learning, including Grid Search, Random Search, Bayesian Optimization, and Genetic Algorithms to improve model performance.
Machine learning models are of critical business importance, and hyperparameters allow us to converge on more accurate models faster. This course will teach you how to understand and optimize hyperparameters by tuning them.
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.
Enhance deep learning skills: master hyperparameter tuning, regularization, optimization, and TensorFlow implementation for improved neural network performance and systematic results generation.
Explore enterprise ML workflow, data management, and model building options using Google Cloud tools. Learn custom training, hyperparameter tuning, and best practices for ML development.
How to Turn Deep Reinforcement Learning Research Papers Into Agents That Beat Classic Atari Games
Learn Support Vector Machines in Python. Covers basic SVM models to Kernel-based advanced SVM models of Machine Learning
Python, Docker, Flask, GitLab, Jenkins tools and technology used for deploy model in your Local server. A complete Guide
Develop and Deploy Machine Learning Web App and Deploy in Python Anywhere Cloud Platform using Python, Flask, Skimage
Explore advanced machine learning algorithms, from regularization to ensemble methods. Master feature engineering, hyperparameter tuning, and model selection for enhanced predictive accuracy and real-world applications.
Comprehensive exploration of Artificial Neural Networks and Convolutional Neural Networks, covering fundamental principles, applications, and practical implementation in diverse domains.
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.
Explore the fundamentals and advanced concepts of Gradient Boost for regression and classification in machine learning.
Learn machine learning techniques for tabular data, including model evaluation, feature engineering, tree-based models, hyperparameter tuning, and neural networks using AWS SageMaker.
Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.
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