AI News, All about the Udacity Machine Learning Nanodegree Program, from a New Graduate!

All about the Udacity Machine Learning Nanodegree Program, from a New Graduate!

Some theory like splitting datasets, cross validation, evaluation metrics for classification, and regression are also discussed here.

Lessons cover some popular approaches like naive bayes, decision tree and random forest, nearest neighbor, svm, and neural networks.

To complete this project, I opted to rely largely on outside resources, and used the Udacity lessons primarily as a guideline for what to cover.

The goal is to apply PCA to construct a lower dimensional feature space and use different clustering techniques like Gaussian and k-means clustering to segment the customers.

The goal is to take a smartcab from a starting point to an end point by giving it feedback through reinforcement learning.

In each trial the smartcab gets assigned an end point goal and has to move there from a given random start point.

I’ve implemented a Q-learning algorithm to teach the smartcab how to reach the goal state while avoiding other agents and avoiding penalties.

Students are supposed to choose a technical problem from a domain they are interested in, write-up a proposal, solve the problem using machine learning techniques, and submit a final report for the project.

Transfer learning refers to the process of using the weights of a pre-trained network trained on a large dataset applied to a different dataset.

As the pre-trained network already knows how to identify lower level features such as edges, lines, and curves, using the weights from it helps to converge to a good score faster than training from scratch.

used a modified VGG-16 model for this competition to extract the features from the convolutional layers and trained a fully connected layer on top of it with batch normalization.

Data augmentation alters our training data by applying random rotations, cropping, flipping, and shifting to prevent overfitting.

Keras interface is easy to use and provides many functionalities for preprocessing and data augmentation during training, so I mostly followed their documentation and tutorials for implementation.

Over the course of the program, I’ve learned that I’m most interested in open-ended projects that I devise on my own, so I’m going to pursue more capstone-level projects going forward.

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