Project Background:
The Deep Boltzmann Machine (DBM) is grounded in pursuing powerful and flexible generative models for complex data. DBMs are a subset of deep learning architectures designed to capture intricate relationships within data by employing multiple layers of latent variables. It stems from the broader context of machine learning evolution driven by the need to model and understand the underlying structures of high-dimensional data. Traditional machine learning models often fall short of capturing these intricate patterns. DBMs represent a breakthrough due to their ability to learn deep, hierarchical representations, making them highly suitable for tasks like image generation, collaborative filtering, and feature learning. This project aims to advance the state of the art in numerous applications, including unsupervised feature learning, generative modeling, and data compression.
1. In this project work, developing more efficient training algorithms to address the computational challenges of deep Boltzmann machines can enable faster model convergence.
2. Advancements in scalable and approximate inference methods make DBMs suitable for large-scale datasets like those encountered in real-world applications.
3. Innovative solutions to the exploration problem in DBM training can enable more effective data space exploration.
4. Addressing scalability issues to handle high-dimensional and multimodal data, broadening the applicability of DBMs across diverse domains.
Generative Modeling: Creating realistic samples from a given data distribution.
Anomaly Detection: Identifying deviations from normal patterns in data.
Collaborative Filtering: Improving recommendation systems by capturing user-item interactions.
Drug Discovery: Generating molecular structures and predicting chemical properties.
Feature Learning: Automatically extracting hierarchical representations of data.
Unsupervised Learning: Discovering patterns and structures without labeled data.
Speech Recognition: Modeling temporal dependencies in audio data.
Dimensionality Reduction: Reducing data dimensionality while preserving important information.
Natural Language Processing: Language modeling, text generation, and semantic representation learning.
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:
