*** AI and Deep-Learning with TensorFlow: https://www.edureka.co/ai-deep-learning-with-tensorflow *** This Edureka PPT provides you with a basic introduction to TensorFlow: The amazing deep learning framework by Google. Below are the topics covered in this PPT: 1. What is TensorFlow? 2. Companies using TensorFlow 3. Features of TensorFlow 4. What are Tensors? 5. What are Neural Networks? 6. TensorFlow Open Source Community Follow us to never miss an update in the future. YouTube: https://www.youtube.com/user/edurekaIN Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Castbox: https://castbox.fm/networks/505?country=in

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TensorFlow is an open source library for Numerical
Computation and Large-Scale Machine Learning

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Adopted Keras
For High-Level APIs
Eager Execution
With Graphs

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Tensors are the standard way of representing data
in deep learning

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Most of the work!
Few lines of code!
Just one line!
Just one line!
Just one line!Collect a dataset Train the Network
Predict
Build your model Evaluate

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import tensorflow as tf
a = tf.variable(0.)
b = tf.variable(1.)
op_add = a.assign(a + b)
op_mul = b.assign(b * 2)
init = tf.global_variables_initializer
with tf.Session() as sess:
init.run()
for iteration in range(50):
sess.run(add_op)
sess.run(mul_op)
print(a.eval())

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import tensorflow as tf
a = tf.variable(0.)
b = tf.variable(1.)
op_add = a.assign(a + b)
op_mul = b.assign(b * 2)
init = tf.global_variables_initializer
with tf.Session() as sess:
init.run()
for iteration in range(50):
sess.run(add_op)
sess.run(mul_op)
print(a.eval())
CONSTRUCTION PHASE
EXECUTION PHASE

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import tensorflow as tf
a = tf.variable(0.)
b = tf.variable(1.)
op_add = a.assign(a + b)
op_mul = b.assign(b * 2)
init = tf.global_variables_initializer
with tf.Session() as sess:
init.run()
for iteration in range(50):
sess.run(add_op)
sess.run(mul_op)
print(a.eval())
import tensorflow as tf
tf.enable_eager_execution()
a = tf.contrib.eager.Variable(0.)
b = tf.contrib.eager.Variable(1.)
for iteration in range(50):
a.assign(a + b)
b.assign(b * 2)
print(a.numpy())

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