By now, you might
already know machine learning, a branch in computer science that studies
the design of algorithms that can learn. Today, you’re going to focus
on deep learning, a subfield of machine learning that is a set of
algorithms that 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, image recognition and
Artificial Intelligence (AI).
Some of the success stories that you might already know are self-driving cars, automatic game play (think of AlphaGo, which plays the board game Go), handwriting generation,…
One of the most powerful and easy-to-use Python libraries for
developing and evaluating deep learning models is Keras; It wraps the
efficient numerical computation libraries Theano and TensorFlow. The
advantage of this is mainly that you can get started with neural
networks in an easy and fun way.
Today’s Keras tutorial for beginners will introduce you to the basics
of Python deep learning: step-by-step, the tutorial will show you how
to use Python and its libraries to explore your data, to build up
multi-layer perceptrons for classification and regression tasks, to
compile and fit the data to these models, to predict target values and
to validate the models that you have built.
Would you like 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 you the six steps that you need to go through to build neural networks in Python with code examples!
Introducing Artificial Neural Networks
Before going deeper into Keras and how you can use it to get started
with deep learning in Python, you should probably know 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 network” can also be used for neurons. The human brain is then
an example of such a neural network, which is composed of a number of
neurons.
And, as you all know, the brain is capable of performing quite
complex computations and this is where the inspiration for Artificial
Neural Networks comes from. The network a whole is a powerful modeling
tool.
Perceptrons
The most simple neural network is the “perceptron”, which, in its
simplest form, consists of a single neuron. Much like biological
neurons, which have dendrites and axons, the single artificial neuron is
a simple tree structure which has input nodes and a single output node,
which is connected to each input node. Here’s a visual comparison of
the two:
链接：
https://www.datacamp.com/community/tutorials/deep-learning-python#gs.SRZpvz4
原文链接：
http://weibo.com/1402400261/F1XZP8gH9?type=comment#_rnd1494155476448
