human
5 Deep Learning (DL)
Of course ! We got two low-level Python libraries: (1) TensorFlow and (2) Theano. Both of them treat data as tensors and construct a computational graph (dataflow paradigm), composed of any mathematical expressions, that get evaluated on CPUs or GPUs. Theano is the pioneer and features an optimizing compiler which will turn the computational graph into efficient code. TensorFlow has a cleaner API (not need to define expressions as strings) and does not require a compilation step (which is painful when developing models).
While you'll only use Theano / TensorFlow to develop DL models, these are the higher-level libraries you'll use to define and test DL architectures on your problem:
* Keras: TensorFlow & Theano backends
* Lasagne: Theano backend
* nolearn: sklearn-like abstraction of Lasagne
* Blocks: Theano backend
* TFLearn: TensorFlow backend"
Please write code following the instructions in jupyter notebook style.

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Can you provide a code to 5 Deep Learning (DL) and the other steps required?
assistant
#Here is code to 5 Deep Learning (DL) and the other steps required.

import os
os.environ['KERAS_BACKEND'] = 'theano'
import keras

class NeuralNet(object):
    
    def __init__(self):
        """Define Neural Network architecture."""
        self.model = keras.models.Sequential()
        self.model.add(keras.layers.Dense(output_dim=46, input_dim=23, activation='relu'))
        self.model.add(keras.layers.Dense(output_dim=1, activation='sigmoid'))
        self.model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])

    def fit(self, X, y):
        y = y / 2 + 0.5  # [-1,1] -> [0,1]
        self.model.fit(X, y, nb_epoch=5, batch_size=32)

    def predict(self, X):
        classes = self.model.predict_classes(X, batch_size=32)
        return classes[:,0] * 2 - 1
        
models.append(NeuralNet())
models[-1].fit(X_train, y_train)

loss_acc = models[-1].model.evaluate(X_test, y_test/2+0.5, batch_size=32)
print('\n\nTesting set: {}'.format(loss_acc))