ianburke1 / tensorflow

Solutions to TensorFlow problem sheet for 4th year module Emerging Technologies

Problem Sheet 4: TensorFlow/Keras

Emerging Technologies - Lecturer: Dr Ian McLoughlin - 4th Year (Hons) Software Development

This repository contains solutions to problem sheet 4 for Emerging Technologies 4th year module. The problem sheet revolves around implementing the famous Iris flower data set using TensorFlow and Keras

Objective

The main objective of this problem sheet is to use Tensorflow/Keras to create a model to predict the species of Iris from a flower’s sepal width, sepal length, petal width, and petal length.

Solution can be found above in TensorFlow_IrisDataSet.ipynb

Technologies Used:

Python

The main programming language used in this problem sheet is Python

Jupyter

Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. It used to be called IPython Notebook but has been renamed and moved to the Jupyter project.

Numpy

NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. It is the fundamental package for scientific computing with Python.

TensorFlow is a Python open source library for fast numerical computing created and released by Google. It is used for machine learning applications such as neural networks. It allows users to express arbitary computation as a graph of data flows. Nodes in this graph represent mathematical operations and the edges represent data that is communicated from one node to another. Data in TensorFlow are represented as tensors which are multidimensional arrays.

Keras is as high level neural network API running on top of TensorFlow. Designed to enable fast and easy experimentation with deep neural networks. Keras is more minimal than TensorFlow as it runs seamlessly on CPU and GPU. It allows for fast prototyping due to its user friendliness.

tensorflow's Issues

Issue used to upload images for readme