Developing Machine Learning Applications with TensorFlow

Talk conducted during Campus DevCon at MSU-IIT Iligan, School of Computer Studies at Andres Bonifacio Ave, Iligan City, Lanao del Norte on May 5, 2018.

Transcript

1. Developing Machine Learning Applications with TensorFlow Marc Anthony Reyes (@marcreyesph) AI Developer Intern, Wela School Systems BS Computer Science, Xavier University – Ateneo de Cagayan
2. About Me • 19 years of age (September 7, 1998) • 3rd Year BS Computer Science • Creative Lead at GDG Cagayan de Oro • Likes The Purge (The First Purge is coming soon in theaters in July) • Loves cats Portions of this slide is using Algorithmia’s Machine Learning presentation. Visit https://blog.algorithmia.com/building-intelligent-applications/ for details.
3. When I had a first glimpse on ML
4. What is Machine Learning?
5. What is Machine Learning? • Arthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term “Machine Learning” in 1959 while at IBM. • “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E” (Mitchell, 1997) Portions of this slide is using Algorithmia’s Machine Learning presentation. Visit https://blog.algorithmia.com/building-intelligent-applications/ for details.
6. Types of Learning • Supervised learning The computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs. • Unsupervised learning No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning) Portions of this slide is using Algorithmia’s Machine Learning presentation. Visit https://blog.algorithmia.com/building-intelligent-applications/ for details.
7. What can Machine Learning do? • Natural Language Processing (NLP) • Sentiment analysis (“I do not like that book”) • Language detection • Image recognition • Cat or dog, model of car, types of objects in frame • Facial recognition (group photos by individual) • Hotdog or not hotdog? • Prediction • Trends (weather, stocks, product sales) • Agents • Automated game players, chatbots Portions of this slide is using Algorithmia’s Machine Learning presentation. Visit https://blog.algorithmia.com/building-intelligent-applications/ for details.
8. Machine Learning Libraries • Natural Language Toolkit (NLTK) • Language processing in Python: Parts of speech, named entities, parse trees • TensorFlow • Open source software library for numerical computation • Flexible architecture • Originally made by researchers and engineers at Google Brain • TensorFlow Lite: Machine Learning apps for android Portions of this slide is using Algorithmia’s Machine Learning presentation. Visit https://blog.algorithmia.com/building-intelligent-applications/ for details.
9. What is TensorFlow? • TensorFlow is an open-source software library for dataflow programming across a range of tasks. It is a symbolic math library, and also used for machine learning applications such as neural networks. • In May 2017 Google announced a software stack specifically for Android development, TensorFlow Lite, beginning with Android Oreo. Portions of this presentation are use Algorithmia’s Machine Learning presentation. Visit https://blog.algorithmia.com/building-intelligent-applications/ for details.
10. What is required from me? • Mathematics • Understanding of Statistics, Linear Algebra (Matrix Operations, Tensors), Calculus • Programming • Knowledge in Python, Scala, Java, or R • Domain Knowledge • Know your problem and your data • Software Engineering • Questions about performance and integration of ML models • Burning passion to pursue ML • Don’t get frustrated if you don’t get it the first time • Practice, practice, practice • Read books Portions of this slide is using Algorithmia’s Machine Learning presentation. Visit https://blog.algorithmia.com/building-intelligent-applications/ for details.
11. Building ML Apps with TensorFlow Eager Execution
12. What is Eager Execution? • TensorFlow's eager execution is an imperative programming environment that evaluates operations immediately, without building graphs. • Operations return concrete values instead of constructing a computational graph to run later.
13. Graph Execution vs Eager Execution Graph Execution import tensorflow as tf sess = tf.InteractiveSession() a = tf.constant(3) b = tf.constant(5) multiply = tf.multiply(a, b) print(multiply) # prints Tensor("Mul_2:0", shape=(), dtype=int32) multiply.eval(feed_dict={a: 3, b: 5}) # prints 15 Eager Execution import tensorflow as tf tf.enable_eager_execution() tf.executing_eagerly() # Prints true a = 3 b = 5 multiply = tf.multiply(a, b) print(multiply) # prints 15
14. What do I need? ML Libraries (e.g. TensorFlow, Theano, Scikit-learn) Data Processing and Visualization Libraries (e.g. matplotlib, ggplot2, numpy, scipy) Interactive IDE (e.g. Jupyter Notebook)
15. Demo: Iris Classification Problem
16. The Iris Classification Problem The Iris genus entails about 300 species, but our program will classify only the following three: Iris setosa, Iris virginica, and Iris versicolor.
17. Things to consider • Make sure your images are of lesser file size for faster processing • Your images should be of the same dimensions; machine learning works on image datasets of the same sizes • Check the version of the data visualization library you’re using • Choosing the right activation function and Machine Learning algorithm for your ML projects (LSTM for NLP, SVM for Image Processing Classification, etc)
18. • Funded by Jollibee FEP Youth Program • Develop an intelligent pest and crop monitoring system via drone imaging • Analyze crop health and whether crops are infested by gathering image data and analyzing them with image processing
19. Tanumbotics (from left to right: Marc Anthony Reyes, Jessa Balagtas, Joseph Philip Fernan Gaston, John Neijzen, Fidel Ivan Racines)
20. Conclusion “It always seems impossible until it is done” –Nelson Mandela
21. is looking for a Web Developer
22. Thank you very much! Social Media / @marcreyesph E-mail / hello@marcreyes.ph