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Thursday, August 16, 2012

Unsupervised Feature Learning and Deep Learning Resources

Unsupervised feature learning and deep learning has been fascinating to me recently and here are some interesting links and tutorials. 

First is Andrew Ng UFLDL tutorial which has some explanation and intuition. It also has starter code where we need to complete only the objective code. 

http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial  by Andrew Ng
 

Reducing the Dimensionality of Data with Neural Networks(pdf)

Self-taught learning(pdf)

An interesting reading about applications of Deep Learning at Google
link1
link2
link3


Google Research ICML paper(pdf)


Transforming Autoencoder(pdf)

Extracting and Composing Robust Features with Denoising Autoencoders(pdf)
(pdf   8 pages)

Greedy Layer-Wise Training of Deep Networks(pdf)

17 comments:

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    1. For Deep Learning Projects for Final Year, unsupervised feature learning is a technique in machine learning where the algorithm learns to extract useful features from raw data without the need for labeled examples. Unlike supervised learning, where the algorithm is trained on labeled data (input-output pairs), unsupervised learning algorithms seek to find patterns and structure in unlabeled data.

      Objective:

      Feature Extraction: The primary goal is to automatically discover or learn features from raw data that are useful for subsequent tasks such as classification, clustering, or anomaly detection.

      No Labels Required: Since the data is unlabeled, the algorithm relies solely on the inherent structure and patterns within the data itself.

      Types of Unsupervised Feature Learning for Image Processing Projects For Final Year Students

      Principal Component Analysis (PCA): A statistical technique that reduces the dimensionality of data while preserving as much variance as possible. PCA identifies orthogonal directions (principal components) that capture the most variation in the data.

      Autoencoders: Neural network models that learn to compress data into a lower-dimensional representation (encoding) and then reconstruct the original data from this representation (decoding). The bottleneck layer in an autoencoder serves as a feature extractor.

      Clustering Algorithms: Such as k-means clustering or hierarchical clustering, which group similar data points together based on their features without prior labels.

      Anomaly Detection: Identifies rare or unusual data points that do not conform to the expected patterns in the data distribution.

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