What is Deep Learning in Python?

Deep Learning in Python is the trending technology in Today’s IT World. Where the programming language Python, used by every software company like Data Science, Google, Salesforce and many more. In particular, Tensor Flow is an open-source library for fast computing of Numerical Elements.

Subsequently, It designed by Google with Apache 2.0 Open Source. As well as, the API used for python programming Language. Moreover, It has Access to C++ API. It is like other Libraries used for Deep Learning (DL) like Theano. TensorFlow Designed for both Development and Research with Production Systems. Consequently, It Operates with Single GPU systems like Mobile Devices and Large Scale Distributed Systems, with Hundreds of machines.

1.Python Deep Learning Libraries:

learn more Deep Learning Algorithms by Deep Learning Online Training

2. How to start Tensor Flow?

Most Important Starting Tensorflow is a simple and easy task with Python Scipy Dashboard. However, Tensorflow works with Python 3.3 or 2.7. As a matter of fact, You have to follow the Installation Guide from Tensorflow Website.

Consequently, You can Do Installation simply by PyPI, which is a Certain set of pip commands. At the same time, you can do it for Linux or Mac OS X dashboard.

3.Examples:

a)Operation:

Especially, An operation is determined as Abstract computation that takes input from Attributes. In addition, It produces output Attributes. For Example, define, multiply and  Finally Add operation.

b)Edges:

The Graph Shows the Flow of Information, looping, branching, and updates for stating. Consequently, the special Edges used for Synchronizing, the behavior that is in the Graph. In fact, waiting for Computation and at the same time completing Inputs.

c)Nodes:

The Nodes operate with computation and they have Zero outputs and Inputs. For Instance, the Information moves in between Nodes, known as tensors with many dimensions, arrays of real and exact values.

4.Linear Regression with TensorFlow:

Generally, the below Example shows how the tensor flow is separating and declaring the computation from the Output.

import tensorflow as tf
import numpy as np
# Create 100 phony x, y data points in NumPy, y = x * 0.1 + 0.3
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data * 0.1 + 0.3
# Try to find values for W and b that compute y_data = W * x_data + b
# (We know that W should be 0.1 and b 0.3, but Tensorflow will
# figure that out for us.)
W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
b = tf.Variable(tf.zeros([1]))
y = W * x_data + b

# Minimize the mean squared errors.
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)
# Before starting, initialize the variables. We will 'run' this first.
init = tf.initialize_all_variables()
# Launch the graph.
sess = tf.Session()
sess.run(init)
# Fit the line.
for step in xrange(201):
sess.run(train)
if step % 20 == 0:
print(step, sess.run(W), sess.run(b))
# Learns best fit is W: [0.1], b: [0.3]
The Output Will be,
(0, array([ 0.2629351], dtype=float32), array([ 0.28697217], dtype=float32))
(20, array([ 0.13929555], dtype=float32), array([ 0.27992988], dtype=float32))
(40, array([ 0.11148042], dtype=float32), array([ 0.2941364], dtype=float32))
(60, array([ 0.10335406], dtype=float32), array([ 0.29828694], dtype=float32))
(80, array([ 0.1009799], dtype=float32), array([ 0.29949954], dtype=float32))
(100, array([ 0.10028629], dtype=float32), array([ 0.2998538], dtype=float32))
(120, array([ 0.10008363], dtype=float32), array([ 0.29995731], dtype=float32))
(140, array([ 0.10002445], dtype=float32), array([ 0.29998752], dtype=float32))
(160, array([ 0.10000713], dtype=float32), array([ 0.29999638], dtype=float32))
(180, array([ 0.10000207], dtype=float32), array([ 0.29999897], dtype=float32))
(200, array([ 0.1000006], dtype=float32), array([ 0.29999971], dtype=float32))

To illustrate, Our Tensor Flow Installation will come with many Deep learning Samples. In order, to utilize and Experiment with Direct Contact. In conclusion, First, you have to check where it was installed on your system.

/usr/lib/python2.7/site-packages/tensor flow

For changing this Directory, you have to take a note of the samples in Subdirectory.

Included with many DL samples.

Generally, Sequence to Sequence Example with an attention mechanism.

End to end, LeNet-5- Like the Convolution MNIST sample.

Especially, CNN for CIFAR-10 Connection.

Multi-thread word2vec that not grouped with Skip-gram Sample.

Finally, Multi-threaded word2vec mini-group skip-gram Sample.