Introduction To TensorFlow | Deep Learning with TensorFlow | TensorFlow For Beginners | Edureka
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** 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
![AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow
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](https://image.slidesharecdn.com/introductiontotensorflow-190117161633/85/Introduction-To-TensorFlow-Deep-Learning-with-TensorFlow-TensorFlow-For-Beginners-Edureka-22-320.jpg)
![AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow
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!](https://image.slidesharecdn.com/introductiontotensorflow-190117161633/85/Introduction-To-TensorFlow-Deep-Learning-with-TensorFlow-TensorFlow-For-Beginners-Edureka-23-320.jpg)
![AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow
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](https://image.slidesharecdn.com/introductiontotensorflow-190117161633/85/Introduction-To-TensorFlow-Deep-Learning-with-TensorFlow-TensorFlow-For-Beginners-Edureka-26-320.jpg)
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Introduction To TensorFlow | Deep Learning with TensorFlow | TensorFlow For Beginners | Edureka
- 1. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow Agenda
- 2. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow 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?
- 3. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow What Is TensorFlow?
- 4. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow 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!
- 5. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow Why Is TensorFlow So Popular?
- 6. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow Popularity Of TensorFlow As simple as pointing your phone!
- 7. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow Popularity Of TensorFlow Scam ProtectionFree Documents too! Millions of happy travelers!
- 8. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow Easiest Way To Get Started!
- 9. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow Get Started! Google Colaboratory! PyCharm
- 10. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow Why Should You Learn TensorFlow?
- 11. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow Why Should You Learn TensorFlow? Learner’s Timeline Train an Image Classifier Work on accuracy Concept-heavy but code-light Work on the purpose!
- 12. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow Some Math! The Math can be overwhelming at start! Programming Rules Data Answers Machine Learning Answers Data Rules
- 13. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow 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!
- 14. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow First Neural Network
- 15. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow 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
- 16. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow 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
- 18. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow 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
- 19. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow TensorFlow Architecture Worker’s Device Interactions Client Master Worker GPU #1 GPU #2 CPU #0 Session Master Create Sessions Execute Operations in the GraphWorker
- 20. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow TensorFlow Pipeline
- 21. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow Simple Code - Pipeline Steps to create a TensorFlow Pipeline Create Data Create Placeholder Define Dataset Create Pipeline Execute Operation
- 22. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow 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
- 23. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow 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!
- 24. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow 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
- 25. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow 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
- 26. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow 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
- 27. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow Conclusion
- 28. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow 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
- 29. AI & Deep Learning Training www.edureka.co/ai-deep-learning-with-tensorflow Conclusion #2