## Deep Learning with TenserFlow workshop.

In this workshop you will learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello World” example, throughout the workshop you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, Deep Neural Networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn popular deep learning API such as Keras and Teano.

**Agenda-**

**· Introduction to Spyder IDE Jupiter Notebook with Python basics**

**· Python Data science libraries**

**· Introduction to tenser flow basics**

**· Basic Neural Networks using tenser flow**

**· Advanced Neural Networks using tenser flow**

**· Keras API for Deep learning**

**· Theano API for Deep learning**

## R Programming Workshop Sri Lanka.

Introduction to R Programming workshop teaches attendees how to use R programming to explore data from a variety of sources by building inferential models and generating charts, graphs, and other data representations.

**Overview**

· History of R

· Advantages and disadvantages

· Downloading and installing

**Introduction**

· Using the R console

· Learning about the environment

· Writing and executing scripts

· Object oriented programming

· Installing packages

· Working directory

· Saving your work

**Variable types and data structures**

· Variables and assignment

· Data types

· Numeric, character, boolean, and factors

· Data structures

· Vectors, matrices, arrays,

· Assigning new values

· Viewing data and summaries

**Base graphics system in R**

· Scatterplots, histograms, barcharts, box and whiskers, dotplots

· Labels, legends, titles, axes

· Exporting graphics to different formats

**General linear regression**

· Linear and logistic models

· Regression plots

· Interaction in regression