Machine Learning with TensorFlow is an introduction to machine learning and TensorFlow. It defines machine learning, describes common machine learning categories and algorithms, and focuses on supervised learning. It introduces TensorFlow as a flexible open-source machine learning library, explains key concepts like tensors and computational graphs, and provides demos of linear regression, neural networks, convolutional neural networks, and using TensorFlow and Keras to implement CNNs. Alternatives to TensorFlow are also listed along with references.

1. Machine Learning with
TensorFlow

2. Machine Learning
Machine Learning is the science of getting computers to
learn and act like humans do, and improve their learning
over time in autonomous fashion, by feeding them data
and information in the form of observations and real-
world interactions [1]

3. Machine Learning Categories
Source: MathWorks

4. Machine Learning
Algorithms
Source: MathWorks

5. Let's focus on supervised learning

6. TensorFlow
TensorFlow is fast, flexible, and scalable open-source
machine learning library for research and production.

7. Key Concepts
•TensorFlow bases its data management in tensors
•Tensors are manipulated using operations
•Each operations are executed in computational graph
•Computational graph are executed with CPU, GPU or TPU

8. Simple Computation with TF
Guess the output!

9. Now, try this

10. What Happened?
•Computational graph is not immediately evaluated when
they are declared
•The graph is executed with tf.Session's run function

11. Linear Regression
•Predict continuous values from previously acquired data
•We try to find linear equation that minimizes the
distance between data points and modeled line

12. Linear Regression
Image source: [2]

13. Demo

14. Artificial Neural Network
•ANN consists of artificial neuron, a mathematical
function modeled after a real biological neuron
•It receives one or more inputs, and sums them to
produce an output
•Additionaly, the sums are normally weighted and the
sum is passed to a nonlinear function (activation
function or transfer function)

15. Perceptron
Image source: [2]

16. Multilayer Perceptron
Image source: [2]

17. Convolution Neural Network
•Part of many of most advanched models currently being
employed.
•Used in numerous fields, but mainly used in image
classifications

18. Convolution
•Introducing kernel, a m x n-dimensional matrix and is
usualy a square matrix (m = n)
•Convolution process consist of multiplying the
corresponding pixels with the kernel and summing the
values for assigning to the central pixel

19. Convolution
Image source: [2]

20. Convolution
The images illustrates how the change
to parameters affect the output
Image source: [2]

21. Subsampling - pooling
•Reduce the quantity and complexity of information while
retaining the most important information elements
•Well known pooling operations are max pool and average
pool

22. Subsampling - pooling
Image source: [2]

23. CNN Architecture: LeNet5
Image source: [2]

24. Demo
Implementing simple CNN with TensorFlow

25. Alternatives to TensorFlow
•Theano
•tf.learn
•tf.keras / keras

26. Demo
Implementing simple CNN with Keras with TensorFlow as
Keras' backend

27. The Tools
•Anaconda (Python interpreter + libraries for scientific
computing)
•Jupyter Notebook (comes with Anaconda)

28. Hi, TensorFlow.js
js.tensorflow.org

29. TensorFlow.js
•Available for Node.js and browsers!
•High-level APIs similar to Keras!

30. References
[1] https://www.techemergence.com/what-is-machine-learning/
[2] Bonnin, Rodolfo. Building Machine Learning Projecs with TensorFlow. Packt
Publishing, 2016.
[3] McClure, Nick. TensorFlow Machine Learning Cookbook. Packt Publishing, 2017.