![]() Or, select Start, then select Settings > System > Power & Sleep > Additional power settings.Use sleep mode when you are going to be away from your computer for a short time. You can quickly resume normal operation within a few seconds. Your computer technically stays on, but all actions on your computer are stopped, any open documents and applications are put in memory. Select Start, then select > Shut down.Learn when to use neural network or tree ensemble models for your task, as these are the two most commonly used supervised learning models in practice today.Build decision trees and tree ensembles, such as random forest and XGBoost (boosted trees) to make predictions.Apply “data-centric AI” to not only tune your model but tune your data (using data synthesis or data augmentation) to improve your model’s performance.Learn to apply the “iterative loop” of machine learning development to train, evaluate, and tune your model.Learn how the “bias-variance trade-off” is different in the age of deep learning, and apply Andrew Ng’s advice for handling bias and variance when training neural networks.Use “learning curves” to determine if your model is experiencing high bias or high variance (or both), and learn which techniques to apply (regularization, adding more data, adding or removing input features) to improve your model’s performance.Choose from various versions of your model using a cross-validation dataset, and evaluate its ability to generalize to real-world data using a test dataset.Discover the value of separating your data set into training, cross-validation, and test sets.Use the advanced “Adam optimizer” to train your model more efficiently.Learn where to use different activation functions (ReLu, linear, sigmoid, softmax) in a neural network, depending on the task you want your model to perform.Build a neural network to perform multi-class classification of handwritten digits in TensorFlow, using categorical cross-entropy loss functions and the softmax activation.Optionally learn how neural network computations are “vectorized” to use parallel processing for faster training and prediction. ![]() Gain a deeper understanding by implementing a neural network in Python from scratch.Build a neural network for binary classification of handwritten digits using TensorFlow.Build and use decision trees and tree ensemble methods, including random forests and boosted trees.Apply best practices for machine learning development so that your models generalize to data and tasks in the real world. ![]()
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