Yes, overfitting can occur in a neural network. There are various ways to handle overfitting in a neural network which are as follows: Dropout Regularization Batch normalization Dropout is a regularization method that approximates training a large number of neural networks with different architectures in parallel. Dropout has the effect of making the training process noisy, forcing nodes within a layer to probabilistically take on more or less responsibility for the inputs. Some of the key aspects for using dropout regularization are: Use with all network types - It can be used with most, perhaps all, types of neural network models, not least the most common network types of Multilayer Perceptrons, Convolutional Neural Networks, and Long Short-Term Memory Recurrent Neural Networks. In the case of LSTMs, it may be desirable to use different dropout rates for the input and recurrent connections. Dropout rate - The default interpretation of the dropout hyperparameter is the probability of training a given node in a layer, where 1.0 means no dropout, and 0.0 means no outputs from the layer. A good value for dropout in a hidden layer is between 0.5 and 0.8. Input layers use a larger dropout rate, such as of 0.8. Grid search parameters – Instead of guessing at a suitable dropout rate for your network, test different rates systematically. For example, test values between 1.0 and 0.1 in increments of 0.1. This will both help discover what works best for our specific model and dataset, as well as how sensitive the model is to the dropout rate. A more sensitive model may be unstable and could benefit from an increase in size. Batch normalisation is a technique for improving the performance and stability of neural networks. The idea is to normalise the inputs of each layer in such a way that they have a mean output activation of 0 and standard deviation of 1. This is analogous to how the inputs to networks are standardised. In Keras, it is implemented using the following code snippet. Note how the BatchNormalization call occurs after each fully-connected layer, but before the activation function and dropout.