How to incorporate Multiple Layers in a Deep Learning model Incorporating multiple layers in a deep learning model can help to improve its performance by allowing it to learn more complex features from the data. A deep learning model consists of layers of artificial neurons, each layer learning a different set of features from …
Month: March 2020
How to use cross_val_score for Cross Validation in Keras Cross-validation is a technique used to evaluate the performance of a machine learning model by dividing the dataset into different subsets and training and evaluating the model on different subsets of the data. This can provide a more robust estimate of the model’s performance. In …
How to use KFold Cross Validation in Keras K-fold cross validation is a technique used to evaluate the performance of a machine learning model by dividing the dataset into k folds or partitions and training the model k times, each time using a different fold as the validation set and the remaining k-1 …
How to use automatic verification within dataset in Keras Automatic verification within a dataset in Keras is a technique used to check the quality and consistency of the data before it is used to train a deep learning model. This can help to ensure that the model is not trained on bad data that …
How to visualize accuracy in Deep Leaning Model in Keras Visualizing the accuracy of a deep learning model in Keras can provide insights into how well the model is performing during training and help identify overfitting or underfitting. The accuracy is a measure of how many predictions made by the model are correct and …
How to visualize loss in Deep Leaning Model in Keras Visualizing the loss of a deep learning model in Keras can provide insights into how well the model is performing during training and help identify overfitting or underfitting. The loss value is a measure of how well the model is able to predict the …
How to use RandomizedSearchCV in Deep Leaning using Keras Randomized search is a technique used to find the best set of hyperparameters for a machine learning model. It is similar to grid search but instead of trying out all possible combinations of hyperparameters, it randomly samples a given number of combinations. It can be …
How to use GridSerachCV in Deep Leaning using Keras Grid search is a technique used to find the best set of hyperparameters for a machine learning model. It involves specifying a range of possible values for each hyperparameter and training the model for each combination of hyperparameter values. The goal is to find the …
How to setup a regression Deep Leaning Model in Keras Regression is a type of supervised machine learning problem where the goal is to predict a continuous value, such as the price of a stock, the temperature, or the weight of an object. In deep learning, a regression model is a neural network that …
How to setup a multiclass classification Deep Leaning Model in Keras Multiclass classification is a type of supervised machine learning problem where the goal is to predict one of multiple possible outcomes. For example, classifying images of animals into different categories such as cats, dogs, lions and so on. In deep learning, a multiclass …