Tensorflow Workshop Content

Learning Objectives

The course is targeted towards two types of participants:
1. Participants that want to use tensorflow for its in-built ML models : At the end of the
workshop, this group should be able to understand tensorflow basics, write basic
computation using tensorflow constructs and use it for building ML models.
2. Participants that want to build custom ML models : At the end of this workshop, this group
should be able to understand how to build custom ML models using tensorflow.

Contents

Basics of Tensorflow – Tensor, variables, placeholders, structure of tensorflow program,
executing tensorflow, TF hierarchy, tensorboard
Machine Learning with Tensorflow – Using high-level estimator API for regression, logistic
regression, DNN, Tutorial using Jupyter notebooks, Introduction to deep playground.
Writing own ML models using Tensorflow – Tensorflow low level APIs, building custom
estimators.

Prerequisite

● We assume familiarity with Python and Machine Learning.
● Participants planning to bring laptop should install Tensorflow and Jupyter notebook on
their laptop.
TensorFlow is the second machine learning framework that Google created and used to design, build, and train deep learning models.You can use the TensorFlow library do to numerical computations, which in itself doesn’t seem all too special, but these computations are done with data flow graphs. In these graphs, nodes represent mathematical operations, while the edges represent the data, which usually are multidimensional data arrays or tensors, that are communicated between these edges.
Dr. Ashish Tendulkar, Google

 

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