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What is Keras?
Keras is HighLevel Deep learning Python library extensively used by Datascientists when it comes to architect the neural networks for complex problems. Higher level API means that Keras can act as front end while you can ask Tensorflow or Theano to work as back end.
Other Deep learning libraries
There are many development options that you can get with TensorFlow and its installation is also quick.

Caffe  Caffe which is really good at speed, implementing the matrix multiplication and ease of use.

Torch  Torch is another Deep learning library written in Lua and C programming. A most sought skill in Data science is ability to work with Torch, besides others. It is lighting fast in implementing the matrix multiplications using numpy as its base data arrays

Tensorflow  Tensorflow is the number one populous deep learning library across the industry till date and it is developed by Google. It uses tensors as the basic operations ( e.g Matrix multiplication )
Implementing Simple Neural Network Using Keras
# Import all the necessary functions to build the neural network
import keras
import keras.layers import Conv1D
from keras.optimizers import Adam
fromkeras.models import sequential
# Lets start building 3 layered convolutional network
def create_model():
model = sequential()
# First layer
model.add(Conv1D ( filters = 10, kernel_size = 10, input_shape, activation = 'relu')
# Second layer
model.add(Conv1D ( filters = 10, Kernel_size = 10, activation = 'relu' )
# third layer
model.add(Conv1D ( filters = 10, kernel_size = 10, activation = 'relu' )
# flatten
model.add(Flatten())
# compile the model
model.compile( loss = 'binary_corssentropy', optimizers = Adam(1e4), metrics= ['accuracy'])
return model
Architecture of Keras
Keras API can be divided into three main categories −

Model

Layer

Core Modules
Keras Models
Keras Models divided into two categories
Sequential Model
The sequential model is basically a linear composition of Keras Layers. Sequential model is easy, minimal as well as has the ability to represent nearly all available neural networks.
A simple sequential model is as follows −
#Import Libraries
from keras.models import Sequential
from keras.layers import Dense, Activation
#Use the Model
model = Sequential()
model.add(Dense(512, activation = 'relu', input_shape = (784,)))
Core Model
Keras also provides a lot of builtin neural network related functions to properly create the Keras model and Keras layers.