Introduction to deep-learning: Glossary

Machine learning is the process where computers learn to recognise patterns of data.

Artificial neural networks are a machine learning technique based on a model inspired by groups of neurons in the brain.

Artificial neural networks can be trained on example data.

Deep Learning is a machine learning technique based on using many artificial neurons arranged in layers.

Deep Learning is well suited to classification and prediction problems such as image recognition.

To use Deep Learning effectively we need to go through a workflow of: defining the problem, identifying inputs and outputs, preparing data, choosing the type of network, choosing a loss function, training the model, tuning Hyperparameters, measuring performance before we can classify data.

Keras is a Deep Learning library that is easier to use than many of the alternatives such as TensorFlow and PyTorch.

The deep learning workflow is a useful tool to structure your approach, it helps to make sure you do not forget any important steps.

Exploring the data is an important step to familiarize yourself with the problem and to help you determine the relavent inputs and outputs.

One-hot encoding is a preprocessing step to prepare labels for classification in Keras.

A fully connected layer is a layer which has connections to all neurons in the previous and subsequent layers.

keras.layers.Dense is an implementation of a fully connected layer, you can set the number of neurons in the layer and the activation function used.

To train a neural network with Keras we need to first define the network using layers and the Model class. Then we can train it using the model.fit function.

Plotting the loss curve can be used to identify and troubleshoot the training process.

The loss curve on the training set does not provide any information on how well a network performs in a real setting.

Creating a confusion matrix with results from a test set gives better insight into the network’s performance.

Separate training, validation, and test sets allows monitoring and evaluating your model.

Batchnormalization scales the data as part of the model.

Convolutional layers make efficient reuse of model parameters.

Pooling layers decrease the resolution of your input

Dropout is a way to prevent overfitting