AI: Custom Deep Neural Network models for OCR (18344) (Mar 2018)

Learning path description

This advanced-level Artificial Intelligence learning path provides instruction on how to build a deep neural network for recognizing characters/digits in an image.

Keywords

Difficulty Level : Advanced
Product : Microsoft Azure
Role : Technical
Country : Global
Essential Mathematics [48 h 0 m]

This course covers the key mathematical concepts required to understand and build machine learning models.

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Basic Statistical background [24 h 0 m]

After covering the basic mathematics, next step is to understand basic statistical concepts that are required to train a machine learning model and interpret the accuracy of the results.

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Basics of Machine Learning [24 h 0 m]

This course covers the theoretical underpinning of machine learning concepts and explains the key machine learning algorithms.

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Machine Learning Programming [54 h 0 m]

This course covers the implementation of machine learning algorithms using data structures available in Python.

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Loading the dataset into an application [0 h 0 m]

For OCR we start with loading an existing dataset of digits. This tutorial shows how to load a digits dataset - MNIST into your Program.

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Learning about a simple classification algorithm [0 h 0 m]

This tutorial shows how to implement a simple and fast classification algorithm (Logistic Regression) to identify and classify different digits in the database

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Applying a moderately better but more complex learning algorithm [0 h 0 m]

We want to improve the accuracy of the prediction. Hence this tutorial shows the implementation of multi-layer perceptron to classify digits.

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Building a Convolution Neural Network for classification [0 h 0 m]

In this tutorial, we implement a Convolution Neural Network for classifying digits in the MNIST database

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Building a Convolution Neural Network for classification for alphabets [0 h 0 m]

We have learnt so far to recognize digits; Now let’s combine this with recognizing English alphabets. This webpage walks through the process of training a Convolution model for recognizing alphabets and digits.

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