** AI & Deep Learning with Tensorflow Training: https://goo.gl/vDxgi5 ** This Edureka tutorial on "Introduction to TensorFlow" provides you an insight into one of the top Deep Learning frameworks that you should consider learning! Check out our Deep Learning blog series: https://bit.ly/2xVIMe1 Check out our complete Youtube playlist here: https://bit.ly/2OhZEpz Follow us to never miss an update in the future. 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

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Agenda

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Agenda
Introduction 01
What is TensorFlow?
Learning 02
Getting Started 03
Basic Code 04
What’s the easiest way to
learn TensorFlow?
Understanding
TensorFlow code!
What’s the fastest way to get
TensorFlow?

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What Is TensorFlow?

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What Is TensorFlow?
Open Source
TensorFlow is an open source library for numerical computation
and large-scale machine learning.
Definition
Available on both
Desktop and Mobile
Largest community
for Learners and
CollaboratorsLet’s get started then!

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Why Is TensorFlow So Popular?

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Popularity Of TensorFlow
As simple as pointing your phone!

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Popularity Of TensorFlow
Scam ProtectionFree Documents too!
Millions of happy travelers!

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Easiest Way To Get Started!

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Get Started!
Google
Colaboratory!
PyCharm

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Why Should You Learn TensorFlow?

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Why Should You Learn TensorFlow?
Learner’s Timeline
Train an Image Classifier Work on accuracy
Concept-heavy but code-light
Work on the purpose!

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Some Math!
The Math can be overwhelming at start!
Programming
Rules
Data
Answers
Machine Learning
Answers
Data
Rules

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Linear Regression – An Example
Let’s have an example!
A simple Linear Regression can
be:
y= w * x + b
Technique
Statistical technique used to
measure the relationship
between the variables
y = 0.1 * x + 0.3 Our Solution!

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First Neural Network

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First Neural Network
Steps to train your first Neural Network
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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TensorFlow Architecture

17. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow
TensorFlow Architecture
What is a Data Flow Graph? Recall the equation y = W * x + b
W - Weight x - Example b - Bias
Biases
Weight
Examples
MatMul
Add ReLu Gradients
Updated
Weights and
Biases

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TensorFlow Architecture
TensorFlow runtime is a cross-platform library
Separates user level code in different languages from core runtime
Construction Phase Execution Phase
CLIENT WORKERS

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TensorFlow Architecture
Worker’s Device Interactions
Client Master Worker
GPU #1 GPU #2 CPU #0
Session
Master Create Sessions
Execute Operations in the GraphWorker

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TensorFlow Pipeline

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Simple Code - Pipeline
Steps to create a TensorFlow Pipeline
Create Data
Create Placeholder
Define Dataset
Create Pipeline
Execute Operation

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Simple Code - Pipeline
Create Data
Create Placeholder
Define Dataset
Create Pipeline
Execute Operation
import numpy as np
x_input = np.random.sample((1,2))
print(x_input)
[[0.8835775 0.23766977]]
MNIST
CIFAR 10

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Simple Code - Pipeline
Create Data
Create Placeholder
Define Dataset
Create Pipeline
Execute Operation
# using a placeholder
x = tf.placeholder(tf.float32, shape=[1,2], name = 'X')
=
It allows us to create our operations and build
our computation graph, without needing the data.
Does it differ, by a little?
YES!

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Simple Code - Pipeline
Create Data
Create Placeholder
Define Dataset
Create Pipeline
Execute Operation
dataset = tf.data.Dataset.from_tensor_slices(x)
Represents a sequence of elements
Tensor 1
Element Tensor 2
Tensor 3

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Simple Code - Pipeline
Create Data
Create Placeholder
Define Dataset
Create Pipeline
Execute Operation
iterator = dataset.make_initializable_iterator()
get_next = iteraror.get_next()
Where should the data flow?
Initialize
Feed the Data
Next Data
Loop

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Simple Code - Pipeline
Create Data
Create Placeholder
Define Dataset
Create Pipeline
Execute Operation
with tf.Session() as sess:
# feed the placeholder with data
sess.run(iterator.initializer, feed_dict={ x: x_input })
print(sess.run(get_next))
[0.8835775 0.23766978]
Initiate a session
Run operation iterator
Feed Numpy values
Populate Placeholder
Print the result

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Conclusion

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Conclusion #1
3 Major Highlights!
tf.keras is basically like Lego building blocks!
Eager Execution Makes TF code as easy as Python
Tf.data LOT OF INFORMATION!
Layers Hyperparameters
Optimizers

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Conclusion #2