Скачать или смотреть Data Science Interview Question Day 5 part 3 Hyperparameter Tuning in Deep Learning

Mastering Hyperparameter Tuning in Deep Learning - A Comprehensive Guide with Examples and Demonstrations

02:35 Hyperparameter tuning in deep learning

Understanding the concept of hyperparameter tuning in deep learning and its agenda
Diving into key hyperparameters and applying hyperparameter tuning in real-world scenarios
05:10 Hyperparameter tuning in deep learning involves selecting the best parameters in machine learning models

Parameters are set prior to the training process and include learning rate, batch size, number of epochs, number of layers, and units in each layer
Understanding key hyperparameters in deep learning is crucial for effective model optimization
07:45 Key hyperparameters in deep learning: Learning Rate and Batch Size

The learning rate controls how much the modeling response changes with each update of model weights
The batch size determines the number of samples used before updating the model
10:20 Hyperparameter tuning in deep learning involves determining the number of samples, reports, layers, units, and dropout rate

Number of samples and reports determine data size processing
Number of layers, units, and dropout rate impact model complexity
12:55 Hyperparameter tuning is crucial for improving model accuracy

Understand the use case and scenario of improving image classification accuracy by classifying images as dogs and cats
Steps for hyperparameter tuning involve choosing parameters like learning rate, batch size, and number of epochs, and defining their range of values
15:30 Optimizing hyperparameters for deep learning models using various search techniques

Define the range of possible values for each hyperparameter
Search for the best combination using techniques like grid search, random search, or Bayesian optimization
18:05 Using Keras tuner for hyperparameter tuning in deep learning

Import and install necessary libraries like Keras tuner, Tensor, and CIFAR-10 dataset
Create and fine-tune the sequential model with specific layers and activation functions
20:38 Hyperparameter tuning for deep learning with early stopping criteria

Choosing the early stopping criteria and the number of trials, setting approach to 50
Searching for the best hyperparameters and evaluating the search trials