Keras Tutorial:deep Learning in Python__python

This is Keras tutorial introduces you to deep learning Python:learn into preprocess to your data, model, evaluate and optimize Neural networks. ▲21▲21 Deep Learning

By now, your might already know machine learning, a branch in computer science that studies the "design of Algorithms" C An learn. Today, your ' re going to focus on deep learning, a subfield of machine learning This is a set of algorithms this is inspired By the structure and function of the brain. These algorithms are usually called Artificial neural Networks (ANN). Deep Learning is one of the hottest fields in data science with many case studies with marvelous results in robotics, imag e Recognition and Artificial Intelligence (AI).

One of the most powerful and easy-to-use Python libraries for developing and evaluating deep learning-is models; It wraps the efficient numerical computation libraries Theano and TensorFlow. The advantage of this is mainly so you can get started with neural networks in a easy and fun way.

Today's Keras tutorial for beginners would introduce you to the basics of Python deep learning:you ' ll-Learn Art Ificial Neural Networks are Then, the tutorial'll show your step-by-step how to use Python and its libraries to Understan D, explore and visualize your data, how to preprocess your data:you ' ll learn I to split up your data in train and test Sets and how can standardize your data, how to build up multi-layer perceptrons for classification tasks, how to Compi Le and fit of the data to this models, how to use your model to predict target values, and how to validate the models that Y OU have built. Lastly, you ' ll also to the can build up a model for regression Tasksand your ' ll learn how can fine-tune the model That's you ' ve built.

Would to take a course on keras and deep learning in Python? Consider taking Datacamp ' s Deep Learning in Python course!

Also, don ' t miss our Keras cheat sheet, which shows for you six steps-you need to go through to build neural In Python with code examples! Introducing Artificial Neural Networks

Before going deeper into keras and how can-use it to-get started with deep learning in Python, you should probably kno W a thing or two about neural networks. As you briefly read in the previous section neural networks found their inspiration and biology, where the term "neural n" Etwork "can also be used for neurons. The human brain is then a example of such a neural network, which is composed of a number of neurons.

And, as all know, the brain are capable of performing quite complex and this is where the computations for Artificial neural Networks comes from. The network a whole is a powerful modeling tool. perceptrons

The most simple neural network are the "Perceptron", which, in its simplest form, and consists of a single neuron. Much like biological neurons, which have dendrites and axons, the single artificial neuron are a simple tree structure whic H has input nodes and a single output node, which are connected to each input node. Here ' s a visual comparison of the two:

As you can there are six components to artificial neurons. From left to right, these are:input nodes. As it so happens, the each input node was associated with a numerical value, which can being any real number. Remember ' real numbers ' up ' full spectrum of numbers:they can is positive or negative, whole or decimal number S. Connections. Similarly, each connection that departs to the input node has a weight associated with it and this can also Number. Next, all the values of the input nodes and weights of the connections are brought Together:they are used as inputs for a  weighted sum:  y=f (∑di=1wi∗xi) y=f (∑I=1DWI∗XI), or, stated differently,  (Y=f w1∗x1+w2∗x2+...wD∗x