This repository serves as a centralized hub for my exploration and development in various STEM fields, alongside topics that align with my academic, professional, and personal interests. It includes resources, notes, projects, and research ideas to document my progress and contributions in these areas. The curated resources and books will serve as foundational building blocks, supporting the achievement of my defined goals.
The repository is categorized into the following areas of focus, each designed to align with my academic and professional goals:
- Biochemistry: Supporting the exploration of biochemical time series and biomedical applications.
- Biology: Providing foundational knowledge for neural science applications and systems modeling.
- Bishop_DL_Figs: Resources from textbooks like Pattern Recognition and Machine Learning to guide projects in neural networks and AI.
- Chemistry: Foundational principles for material science and nanotechnology.
- Computer Science: Practical guides and theories to master algorithms, compiler design, IoT systems, and machine learning frameworks.
- Electrical and Computer Engineering: Resources for designing graphene-based chips and IoT architectures.
- Material Science: Exploring innovative materials like graphene and their application in cutting-edge chip designs.
- Mathematics: Essential for understanding physical models, differential equations, and advanced algorithms.
- Misc: A flexible section for interdisciplinary topics or areas of curiosity.
- Nanotechnology: Deep dives into nanoscale material development and device applications.
- Physics: Bridging physical theories with applications in material science and electronics.
- Research Papers: An organized collection of academic papers for inspiration and research methodology.
- Time Series: Applying time-series analysis in both biological systems and financial models.
- Psychology & Neural Science: Cognitive science resources to inspire AI architectures.
This repository is built to bridge theoretical knowledge with practical applications in the following ways:
- Goals:
- Build foundational understanding in algorithms, compiler design, and advanced machine learning techniques.
- Research and implement edge computing architectures for IoT.
- Resources:
- Books and papers in the Computer Science and Electrical and Computer Engineering categories provide theoretical insights and code examples.
- Bishop’s Pattern Recognition and Machine Learning for neural network design and cognitive AI applications.
- Goals:
- Deepen knowledge in differential equations, linear algebra, and their applications.
- Apply advanced mathematical principles to nanotechnology and material science problems.
- Resources:
- Textbooks and notes in the Mathematics section for mastering essential topics.
- Research papers in the Time Series section for real-world applications.
- Goals:
- Explore theoretical and practical aspects of graphene-based chip development.
- Investigate the interplay between physics, material science, and electrical engineering in nanotechnology.
- Resources:
- Books and journals in the Physics and Material Science sections, focused on state-of-the-art technologies and applications.
- Goals:
- Explore the biophysical properties of materials for biomedical applications.
- Study biochemical time series and neural science intersections.
- Resources:
- Foundational knowledge from Biology, Chemistry, and Biochemistry sections to guide experiments and modeling efforts.
- Goals:
- Analyze the intersection of neuroscience, psychology, and machine learning.
- Develop systems inspired by cognitive and neural architectures.
- Resources:
- The Psychology & Neural Science category includes books, studies, and notes for understanding cognition and implementing AI.
This repository:
- Centralizes Resources: It consolidates books, research papers, and tools across disciplines to eliminate fragmented learning.
- Supports Learning and Experimentation: Provides theoretical knowledge and practical resources to explore novel concepts in STEM.
- Enables Cross-Disciplinary Insights: Encourages connections between fields (e.g., neural science and machine learning, material science and physics).
- Documents Progress: Tracks research, experimentation, and results to build a solid foundation for future projects and collaborations.
This repository is an evolving project. Contributions, feedback, or discussions about the resources, goals, or methodologies are welcome.
Topics and resources discussed are based on:
- Research and theories across STEM fields.
- Conversations on Git management, differential equations, neural networks, and IoT applications.
- Books and academic resources spanning my interests, integrated into my professional goals.