Learn Artificial Neural Networks (ANN) in R. Build predictive deep learning models using Keras and Tensorflow R Studio R interface to Keras Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Keras has the following key features: Allows the same code to run on CPU or on GPU, seamlessly. User-friendly API which makes it easy to quickly prototype deep learning models. This means that Keras is appropriate for building essentially any deep learning model, from a memory network to a neural Turing machine.

Artificial intelligence and machine learning haven't just grabbed headlines and made for blockbuster movies; they're poised to make a real difference in our everyday lives, such as with self-driving cars and life-saving medical devices. In fact, according to Global Big Data Conference, AI is "completely reshaping life sciences, medicine, and healthcare" and is also transforming voice-activated assistants, image recognition and many other popular technologies. Artificial Intelligence is a term used for machines that can interpret the data, learn from it, and use it to do such tasks that would otherwise be performed by humans. Machine Learning is a branch of Artificial Intelligence which focuses more on training the machines to learn on their own without much supervision. What is a neural network?

In the neural network tutorial, I introduced the gradient descent algorithm which is used to train the weights in an artificial neural network. In reality, for deep learning and big data tasks standard gradient descent is not often used. Rather, a variant of gradient descent called stochastic gradient descent and in particular its cousin mini-batch gradient descent is used. That is the focus of this post. The gradient descent optimisation algorithm aims to minimise some cost/loss function based on that function's gradient.

Recommended online course: If you like video courses, I'd recommend the following inexpensive Udemy course on neural networks: Deep Learning A-Z: Hands-On Artificial Neural Networks Here's an outline of the tutorial, with links, so you can easily navigate to the parts you want: Artificial neural networks (ANNs) are software implementations of the neuronal structure of our brains. In a supervised ANN, the network is trained by providing matched input and output data samples, with the intention of getting the ANN to provide a desired output for a given input. As mentioned previously, biological neurons are connected hierarchical networks, with the outputs of some neurons being the inputs to others. These structures can come in a myriad of different forms, but the most common simple neural network structure consists of an input layer, a hidden layer and an output layer.