This advanced level Artificial Intelligence learning path provides instruction on how to build a custom Deep Neural network for object detection and classification.
This course covers the key mathematical concepts required to understand and build machine learning models.
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.
This course covers the theoretical underpinning of machine learning concepts and explains the key machine learning algorithms.
This course covers the implementation of machine learning algorithms using data structures available in Python.
This course provides an introduction to Deep Neural Network and covers implementation of a DNN using Microsoft Cognitive Tool Kit.
This talk outlines the key aspects of building an object detection mode using CNTK.
This page provides details on loading the CIFAR-10 dataset for training a Deep Neural Network.
This tutorial provides the details to implement a Convolution Neural Network in CNTK to identify and classify the images of different objects.
In the previous tutorial, you trained a CNN model from scratch. With Transfer Learning, you can take an existing trained model and adapt it to your own specialized domain.
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