How to setup a binary classification Deep Leaning Model in Keras Binary classification is a type of supervised machine learning problem where the goal is to predict one of two possible outcomes, such as “yes” or “no”, “true” or “false”, or “0” or “1”. In deep learning, a binary classification model is a …
Month: March 2020
How to split train and test datasets using validation_split in Keras When training a deep learning model, it’s important to evaluate its performance on unseen data, to make sure it generalizes well to new examples. To do this, you need to split your data into a training set, which is used to train the …
How to split train and test datasets in a Deep Leaning Model in Keras When training a deep learning model, it’s important to evaluate its performance on unseen data, to make sure it generalizes well to new examples. To do this, you need to split your data into a training set, which is used …
How to setup Early Stopping in a Deep Learning Model in Keras Early stopping is a technique used to stop training a deep learning model when the performance of the model on a validation dataset stops improving. The idea behind early stopping is to prevent overfitting by stopping the training process before the model …
How to create FeedForward Neural Networks in Keras A feedforward neural network, also known as a multi-layer perceptron (MLP), is a type of neural network that consists of an input layer, one or more hidden layers, and an output layer. Each layer is made up of a set of neurons, which are connected …
How to use VarianceScaling initializer to a Deep Learning Model in Keras In deep learning, the initializer is a method used to set the initial values of the weights of the neural network. The initial values of the weights play a crucial role in the training process, as they determine how the network will …
How to use RandomNormal initializer in a Deep Learning Model in Keras In deep learning, the initializer is a method used to set the initial values of the weights of the neural network. The initial values of the weights play a crucial role in the training process, as they determine how the network will …
How to use l1_l2 regularization to a Deep Learning Model in Keras In deep learning, weight regularization is a technique used to prevent overfitting by adding a penalty term to the loss function. There are different types of weight regularization, but one of the most common is L1 regularization and L2 regularization. L1 regularization …
In deep learning, weight regularization is a technique used to prevent overfitting by adding a penalty term to the loss function. There are different types of weight regularization, but one of the most common is L2 regularization, also known as weight decay. L2 regularization adds a penalty term to the loss function that …
In deep learning, a dropout layer is a regularization technique that helps prevent overfitting by randomly dropping out (or turning off) a certain number of neurons during training. In other words, dropout layer randomly drops out a certain percentage of neurons in a layer, so that the neurons in the layer are not …