deep learning with python
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Deep Learning is the trending technology in Today’s IT Sector. Where the language Python, used by every software company like Data Science, Google, Salesforce and many more. In our Blog Deep learning with Python, you find the best path to master them.

What is Deep Learning?

Generally, Deep Learning is another section of Machine Learning. It works with Deep Learning algorithms that functioned and designed by Brain known as Artificial Neural Networks.

What is Python?

Learn Python is a High-level and object oriented programming language. In the same fashion with Integrated Dynamic Semantics for Developing Application and Development.

Deep Learning with Python

Python Deep Learning Libraries

  • Spark-Deep Learning
  • Elephas
  • Nolearn
  • Lasagne
  • TFLearn
  • CNTK
  • Caffe
  • Theano
  • Apache MXnet
  • PyTorch
  • Tensor flow.
  • Moreover Distributed Keras

Now we see DL with Python machine learning by Tensor Flow.

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. In other words 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. In particular, consequently It Operates with Single GPU systems like Mobile Devices and Large Scale Distributed Systems with Hundreds of machines.

How you have to start Tensor Flow?

Most Important Starting Tensorflow is simple and afterwards it is a 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 Certain set of pip Command. So, At the same time, For using your Linux or with Mac OS X dashboard on Download and Starting Web page.



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


In the same Fashion, 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 Inputs for Completion with Online Education.


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

Linear Regression with TensorFlow

Generally, below Example shows how the tensor flow is separating and declaration 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()
# Fit the line.
for step in xrange(201):
if step % 20 == 0:
# 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, you utilize and Experiment with Direct Contact. In conclusion First, you have to check where it was installed on your System.

For Instance, this include

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

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

Included many numbers of 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.

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