Machine learning using TensorFlow on Data Science Experience Platform (DSX)

1. Lightning Talk: Machine Learning using TensorFlow on DSX
Tuhin Mahmud
Big Data AI Revealed Meetup
Austin,TX
Oct 26th,2017

2. Agenda
● DSX - Data Science Experience
● Machine Learning
○ K Mean Clustering
○ Linear Classifier
● Machine Learning with TensorFlow on DSX

3. DSX - IBM Data Science Experience
https://datascience.ibm.com/
● Use your own data to create, train, and deploy self-learning models. Leverage an automated, collaborative workflow
to drive intelligence into day-to-day business applications easily and with more confidence.
● Select Data
● Train and Validate
● Deploy

4. DSX + PowerAI :Deep Learning Frameworks

5. TensorFlow
K- Mean Clustering Example
● tf.constant(pd_1.as_matrix())
● tf.get_variable()
● tf.expand_dims()
● Tf.reduce_sum()
● Tf.square()
● tf.subtract()
● Tf.argmin()
● tf.dynamic_partition()
● tf.reduce_mean()
● tf.concat()
● tf.assign()
● tf.global_variables_initializer()
● tf.Session()
● sess.run(init)
Linear Classifier Example
● tf.SparseTensor()
● tf.constant)_
● tf.contrib.layers.sparse_column_with_hash
_bucket()
● tf.contrib.layers.real_valued_column()
● tf.contrib.learn.LinearClassifier()
○ default Ftrl optimizer will be used
○ https://www.tensorflow.org/api_docs/pytho
n/tf/train/FtrlOptimizer
● m.fit()
● m.evaluate()
“An interface for expressing machine learning algorithms
and an implementation for executing such algorithms
A framework for creating ensemble algorithms for today’s most challenging
problems “ - Google Brain Team

6. K Mean Clustering
k-means clustering aims to partition n observations into k clusters in which each
observation belongs to the cluster with the nearest mean. [source: Wikipedia]

7. Linear Classifier
A linear classifier achieves this by making a classification decision based on the value of a linear
combination of the characteristics[wikipedia]
For a two-class classification problem, one can visualize the operation of a linear classifier as splitting a
high-dimensionalinput space with a hyperplane: all points on one side of the hyperplane are classified as
"yes", while the others are classified as "no".

8. Notebooks
● Real Estate Price Estimation
○ https://dataplatform.ibm.com/analytics/notebooks/581c958e-bb1e-46d3-9322-
a35691f4c58d/view?access_token=3605d00df6153d0b0f8a8457dec8f57eaf15e493e11b9a2b
50d352a00a7432e6
● K Mean Clustering Example
○ https://dataplatform.ibm.com/analytics/notebooks/d7b69f25-40fe-4028-83c3-
70cbd85b8009/view?access_token=1c80c4f653fefd6c5860925bd766beaca9e7e8156e58ff1d0
d5fbcb21dab60eb
● Linear Classifier example
○ https://dataplatform.ibm.com/analytics/notebooks/5ce96896-c3cd-46de-b3d9-

9. Demo

10. Next Meetup
https://www.meetup.com/Big-Data-AI-Revealed-Austin/events/244457206/
Topic: Real-time Sporting Event Prediction - DL and ML Lecture Series -2
● Date: Nov 30th, 2017
● Venue: Capital Factory 701 Brazos St,16th Floor, Austin, TX
Lightning Talks - (5 - 10) mins
● General Topics of interest in the area of Machine learning, AI, Big Data
● Access for any member of the meetup , discuss any tutorials,presentation or his works.
Announcements
Big Data AI Revealed

11. Thank you.