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In deep learning with Keras, you don’t have to code a lot, but there are a few steps on which you need to step over slowly so that in the near future, you can create your models. The flow of modelling is to load data, define the Keras model, compile the Keras model, fit the Keras model, evaluate it, tie everything together, and make the predictions out of it.

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But at times, you might find it confusing because of not having a good hold on the fundamentals of deep learning. Before starting your new deep learning with Keras project, make sure to go through this ultimate guide which will help you in revising the fundamentals of deep learning with Keras. 

In the field of Artificial Intelligence, deep learning has become a buzzword which always finds its way in various conversations. When it comes to imparting intelligence to the machines, it has been since many years that we used Machine Learning (ML).

But, considering the current period, due to its supremacy in predictions, deep learning with Keras has become more liked and famous as compared to the old and traditional ML techniques. 

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Deep Learning

Machine learning has a subset in which the Artificial Neural Networks (ANN) is trained with a large amount of data. This subset is nothing but deep learning. Since a deep learning algorithm learns from experience, it performs the task repeatedly; every time it tweaks it a little intending to improve the outcome.

It is termed as ‘deep learning’ because the neural networks have many deep layers which enables learning. Deep learning can solve any problem in which thinking is required to figure out the problem. It is widely used in various fields, including application of CNN for image recognition and pattern detection.

Read: Top Deep Learning Techniques

Keras 

There are many APIs, frameworks, and libraries available to get started with deep learning. But here’s why deep learning with Keras is beneficial. Keras is a high-level neural network application programming interface (API) which runs on the top of TensorFlow – which is an end-to-end machine learning platform and is an open-source. Not just Tensorflow, but also CNTK, Theano, PlaidML, etc.

It helps in commoditizing artificial intelligence (AI) and deep learning. The coding in Keras is portable, it means that using Keras you can implement a neural network while using Theano as a backend and then subsequently run it on Tensorflow by specifying the backend. Also further, it is not mandatory rather, not needed at all to change the code. 

If you are wondering why deep learning is an important term in Artificial Intelligence or if you are lagging motivation to start learning deep learning with Keras, this google trends snap shows how people’s interest in deep learning has been growing steadily worldwide for the last few years.

There are many areas like natural language processing (NLP), computer vision, speech recognition, drug design, bioinformatics, etc where deep learning has been applied successfully. 

Getting Into Deep Learning with Keras

As discussed earlier, in deep learning, artificial neural networks are trained with a large amount of data. On getting trained, the predictions on unseen data can be easily availed by the network. Given below are a few important terms usually used in training a neural network required in deep learning with Keras.

Also read: 15 Interesting Machine Learning Project Ideas For Beginners

Neural Networks

It was neural networks in our brain which gave rise to the idea of artificial neural networks. Usually, there are three layers in a neural network. These three layers are – input layer, output layer, and hidden layer. An example of the neural network is as shown in the figure below.

Since the neural network shown above only consists of one hidden layer, it is termed as a “shallow neural network”. More hidden layers are added in such architectures to create more complex architectures.

Deep Networks

In a deep network, there are multiple hidden layers added. When the number of hidden layers increases in the network, the training of such architectures becomes complex not only in terms of the time taken by it to train the network fully, but it also requires more resources. Given below is a deep network which consists of an input, four hidden layers, and an output.

Network Training

Once your network architecture has been defined, it needs training for certain kinds of predictions. In the training process of a network, the proper weights for each link are found for the network.

While the training is going on, the data flows in the standard direction, that is, from the input to the output layer and passes many hidden layers. This network is called a Feed-Forward Network, similar to the application of CNN in deep learning models.

Activation Function

At one and all layers, you need to calculate the weighted sum of the inputs and pass it to an Activation function. This activation function makes the network nonlinear. It is simply a mathematical function which takes the output and discretizes it. Few of the most commonly used activation functions are hyperbolic, sigmoid, tangent (tanh), Softmax, and ReLU.

Backpropagation

In backpropagation, the propagation of errors is in backward direction, that is from the output to the input layer. In supervised learning, backpropagation is an algorithm. For a given error function, the gradient of the error function is calculated with respect to the assigned weight at each connection.

Through the network, even the calculation of gradient proceeds in the backward direction. The last layer gradient of weight is calculated first and the first layer gradient of weight is calculated at the last. 

At every layer, in determining the gradient for the preceding layer, it reuses the partial calculations of the gradient. This is nothing but “Gradient Descent”. 

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Defining the model in deep learning with Keras

Defining the model in deep learning can be divided into several characteristics:

1. Number of layers.
2. Types of layers.
3. Number of neurons in each layer.
4. Activation function of each layer.
5. Input size and Output size.

Read: Regularization in Deep Learning

The Bottom Line

Learning deep learning with Keras is essential because Keras is an easy to use and a powerful open source and free python library for evaluating and developing deep learning models. Keras also wraps the widely used numerical computation libraries like TensorFlow and Theano which allows you to train as well as define the neural network models with the need of just a few lines of programming code.

Once you have python 2 or 3 installed and configured, you are all set to start with your deep learning with Keras project. You just need to add SciPy installed and configured, including NumPy and have Keras with a TensorFlow or Theano backend installed and configured. 

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