Project Background:
Restricted Boltzmann Machines (RBMs) have become a fundamental building block in deep learning. Their origins can be traced back to statistical physics, where they were initially introduced as a type of Markov random field. The transformative role was solidified with the development of contrastive divergence, an efficient training algorithm for RBMs. RBMs are powerful feature learning and generative modeling tools in unsupervised learning scenarios. They have found applications in diverse domains. Furthermore, RBMs are often employed in pre-training deep neural networks. They are enabling the creation of more effective and accurate deep-learning models. Understanding the background of RBMs is essential for harnessing their capabilities in various tasks and exploring their potential in emerging research areas.
1. In this project, RBMs are adept at automatically discovering relevant and informative features from complex and high-dimensional data, making them invaluable in applications where feature extraction is critical.
2. Serve as a generative model and allow the creation of new data samples that resemble the underlying training data distribution. This capability has opened up possibilities in data synthesis and augmentation.
3. Additionally, RBMs make learned features and model decisions more interpretable of model transparency. Their utility extends to hierarchical feature representations in deep networks and privacy-preserving learning, demonstrating their versatility and wide-ranging impact in the machine learning community.
Collaborative Filtering: Enhancing recommendation systems by modeling user-item interactions.
Dimensionality Reduction: Reducing the number of features in data while preserving information.
Classification: Serving as building blocks in deep learning architectures for supervised tasks.
Deep Belief Networks: Used as components in deep learning models particularly in pre-training.
Generative Modeling: Generating realistic samples from a given data distribution.
Topic Modeling: Discovering latent topics in large document collections.
Representation Learning: Capturing meaningful and compact representations of data.
Financial Time Series Analysis: Used in model dependencies for tasks like stock price prediction.
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
