1570417633
TensorFlow is a Python library for fast numerical computing created and released by Google.
It is a foundation library that can be used to create Deep Learning models directly or by using wrapper libraries that simplify the process built on top of TensorFlow.
In this post you will discover the TensorFlow library for Deep Learning.
Discover how to develop deep learning models for a range of predictive modeling problems with just a few lines of code in my new book, with 18 step-by-step tutorials and 9 projects.
TensorFlow is an open source library for fast numerical computing.
It was created and is maintained by Google and released under the Apache 2.0 open source license. The API is nominally for the Python programming language, although there is access to the underlying C++ API.
Unlike other numerical libraries intended for use in Deep Learning like Theano, TensorFlow was designed for use both in research and development and in production systems, not least RankBrain in Google search and the fun DeepDream project.
It can run on single CPU systems, GPUs as well as mobile devices and large scale distributed systems of hundreds of machines.
You may also like: Deep Learning Using TensorFlow
Installation of TensorFlow is straightforward if you already have a Python SciPy environment.
TensorFlow works with Python 2.7 and Python 3.3+. You can follow the Download and Setup instructions on the TensorFlow website. Installation is probably simplest via PyPI and specific instructions of the pip command to use for your Linux or Mac OS X platform are on the Download and Setup webpage.
There are also virtualenv and docker images that you can use if you prefer.
To make use of the GPU, only Linux is supported and it requires the Cuda Toolkit.
Computation is described in terms of data flow and operations in the structure of a directed graph.
This first example is a modified version of the example on the TensorFlow website. It shows how you can create a session, define constants and perform computation with those constants using the session.
import tensorflow as tf
sess = tf.Session()
a = tf.constant(10)
b = tf.constant(32)
print(sess.run(a+b))
Running this example displays:
42
This next example comes from the introduction on the TensorFlow tutorial.
This examples shows how you can define variables (e.g. W and b) as well as variables that are the result of computation (y).
We get some sense of TensorFlow separates the definition and declaration of the computation from the execution in the session and the calls to run.
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]
Running this example prints the following output:
(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))
You can learn more about the mechanics of TensorFlow in the Basic Usage guide.
Your TensorFlow installation comes with a number of Deep Learning models that you can use and experiment with directly.
Firstly, you need to find out where TensorFlow was installed on your system. For example, you can use the following Python script:
python -c 'import os; import inspect; import tensorflow; print(os.path.dirname(inspect.getfile(tensorflow)))'
For example, this could be:
/usr/lib/python2.7/site-packages/tensorflow
Change to this directory and take note of the models subdirectory. Included are a number of deep learning models with tutorial-like comments, such as:
Also check the examples directory as it contains an example using the MNIST dataset.
There is also an excellent list of tutorials on the main TensorFlow website. They show how to use different network types, different datasets and how to use the framework in various different ways.
Finally, there is the TensorFlow playground where you can experiment with small networks right in your web browser.
In this post you discovered the TensorFlow Python library for deep learning.
You learned that it is a library for fast numerical computation, specifically designed for the types of operations that are required in the development and evaluation of large deep learning models.
Deploying a Keras Deep Learning Model as a Web Application in Python
Deep Learning With TensorFlow 2.0
#deep-learning #tensorflow #python
1624291780
This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you’ll be a python programmer in no time!
⭐️ Contents ⭐
⌨️ (0:00) Introduction
⌨️ (1:45) Installing Python & PyCharm
⌨️ (6:40) Setup & Hello World
⌨️ (10:23) Drawing a Shape
⌨️ (15:06) Variables & Data Types
⌨️ (27:03) Working With Strings
⌨️ (38:18) Working With Numbers
⌨️ (48:26) Getting Input From Users
⌨️ (52:37) Building a Basic Calculator
⌨️ (58:27) Mad Libs Game
⌨️ (1:03:10) Lists
⌨️ (1:10:44) List Functions
⌨️ (1:18:57) Tuples
⌨️ (1:24:15) Functions
⌨️ (1:34:11) Return Statement
⌨️ (1:40:06) If Statements
⌨️ (1:54:07) If Statements & Comparisons
⌨️ (2:00:37) Building a better Calculator
⌨️ (2:07:17) Dictionaries
⌨️ (2:14:13) While Loop
⌨️ (2:20:21) Building a Guessing Game
⌨️ (2:32:44) For Loops
⌨️ (2:41:20) Exponent Function
⌨️ (2:47:13) 2D Lists & Nested Loops
⌨️ (2:52:41) Building a Translator
⌨️ (3:00:18) Comments
⌨️ (3:04:17) Try / Except
⌨️ (3:12:41) Reading Files
⌨️ (3:21:26) Writing to Files
⌨️ (3:28:13) Modules & Pip
⌨️ (3:43:56) Classes & Objects
⌨️ (3:57:37) Building a Multiple Choice Quiz
⌨️ (4:08:28) Object Functions
⌨️ (4:12:37) Inheritance
⌨️ (4:20:43) Python Interpreter
📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=rfscVS0vtbw&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=3
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Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!
#python #learn python #learn python for beginners #learn python - full course for beginners [tutorial] #python programmer #concepts in python
1618317562
View more: https://www.inexture.com/services/deep-learning-development/
We at Inexture, strategically work on every project we are associated with. We propose a robust set of AI, ML, and DL consulting services. Our virtuoso team of data scientists and developers meticulously work on every project and add a personalized touch to it. Because we keep our clientele aware of everything being done associated with their project so there’s a sense of transparency being maintained. Leverage our services for your next AI project for end-to-end optimum services.
#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services
1625843760
When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.
When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,
#machine learning #sql server #executing python in sql server #machine learning using python #machine learning with sql server #ml in sql server using python #python in sql server ml #python packages #python packages for machine learning services #sql server machine learning services
1619510796
Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map
1570417633
TensorFlow is a Python library for fast numerical computing created and released by Google.
It is a foundation library that can be used to create Deep Learning models directly or by using wrapper libraries that simplify the process built on top of TensorFlow.
In this post you will discover the TensorFlow library for Deep Learning.
Discover how to develop deep learning models for a range of predictive modeling problems with just a few lines of code in my new book, with 18 step-by-step tutorials and 9 projects.
TensorFlow is an open source library for fast numerical computing.
It was created and is maintained by Google and released under the Apache 2.0 open source license. The API is nominally for the Python programming language, although there is access to the underlying C++ API.
Unlike other numerical libraries intended for use in Deep Learning like Theano, TensorFlow was designed for use both in research and development and in production systems, not least RankBrain in Google search and the fun DeepDream project.
It can run on single CPU systems, GPUs as well as mobile devices and large scale distributed systems of hundreds of machines.
You may also like: Deep Learning Using TensorFlow
Installation of TensorFlow is straightforward if you already have a Python SciPy environment.
TensorFlow works with Python 2.7 and Python 3.3+. You can follow the Download and Setup instructions on the TensorFlow website. Installation is probably simplest via PyPI and specific instructions of the pip command to use for your Linux or Mac OS X platform are on the Download and Setup webpage.
There are also virtualenv and docker images that you can use if you prefer.
To make use of the GPU, only Linux is supported and it requires the Cuda Toolkit.
Computation is described in terms of data flow and operations in the structure of a directed graph.
This first example is a modified version of the example on the TensorFlow website. It shows how you can create a session, define constants and perform computation with those constants using the session.
import tensorflow as tf
sess = tf.Session()
a = tf.constant(10)
b = tf.constant(32)
print(sess.run(a+b))
Running this example displays:
42
This next example comes from the introduction on the TensorFlow tutorial.
This examples shows how you can define variables (e.g. W and b) as well as variables that are the result of computation (y).
We get some sense of TensorFlow separates the definition and declaration of the computation from the execution in the session and the calls to run.
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]
Running this example prints the following output:
(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))
You can learn more about the mechanics of TensorFlow in the Basic Usage guide.
Your TensorFlow installation comes with a number of Deep Learning models that you can use and experiment with directly.
Firstly, you need to find out where TensorFlow was installed on your system. For example, you can use the following Python script:
python -c 'import os; import inspect; import tensorflow; print(os.path.dirname(inspect.getfile(tensorflow)))'
For example, this could be:
/usr/lib/python2.7/site-packages/tensorflow
Change to this directory and take note of the models subdirectory. Included are a number of deep learning models with tutorial-like comments, such as:
Also check the examples directory as it contains an example using the MNIST dataset.
There is also an excellent list of tutorials on the main TensorFlow website. They show how to use different network types, different datasets and how to use the framework in various different ways.
Finally, there is the TensorFlow playground where you can experiment with small networks right in your web browser.
In this post you discovered the TensorFlow Python library for deep learning.
You learned that it is a library for fast numerical computation, specifically designed for the types of operations that are required in the development and evaluation of large deep learning models.
Deploying a Keras Deep Learning Model as a Web Application in Python
Deep Learning With TensorFlow 2.0
#deep-learning #tensorflow #python