@BOOK{Pytorch_Book,
  author={Jha, Ashish Ranjan and Pillai, Dr. Gopinath},
  booktitle={Mastering PyTorch: Build powerful neural network architectures using advanced PyTorch 1.x features},
  year={2021},
  volume={},
  number={},
  pages={},
  keywords={},
  doi={}
}

@book{Gazit_2024, 
    address={Birmingham}, 
    edition={1}, 
    title={Mastering NLP from Foundations to LLMs: Apply advanced rule-based techniques to LLMs and solve real-world business problems using Python}, 
    ISBN={9781804616383}, 
    abstractNote={Enhance your NLP proficiency with modern frameworks like LangChain, explore mathematical foundations and code samples, and gain expert insights into current and future trends Key FeaturesLearn how to build Python-driven solutions with a focus on NLP, LLMs, RAGs, and GPT Master embedding techniques and machine learning principles for real-world applications Understand the mathematical foundations of NLP and deep learning designs Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDo you want to master Natural Language Processing (NLP) but don't know where to begin? This book will give you the right head start. Written by leaders in machine learning and NLP, Mastering NLP from Foundations to LLMs provides an in-depth introduction to techniques. Starting with the mathematical foundations of machine learning (ML), you'll gradually progress to advanced NLP applications such as large language models (LLMs) and AI applications. You'll get to grips with linear algebra, optimization, probability, and statistics, which are essential for understanding and implementing machine learning and NLP algorithms. You'll also explore general machine learning techniques and find out how they relate to NLP. Next, you'll learn how to preprocess text data, explore methods for cleaning and preparing text for analysis, and understand how to do text classification. You'll get all of this and more along with complete Python code samples. By the end of the book, the advanced topics of LLMs' theory, design, and applications will be discussed along with the future trends in NLP, which will feature expert opinions. You'll also get to strengthen your practical skills by working on sample real-world NLP business problems and solutions.What you will learnMaster the mathematical foundations of machine learning and NLP Implement advanced techniques for preprocessing text data and analysis Design ML-NLP systems in PythonModel and classify text using traditional machine learning and deep learning methodsUnderstand the theory and design of LLMs and their implementation for various applications in AIExplore NLP insights, trends, and expert opinions on its future direction and potentialWho this book is forThis book is for deep learning and machine learning researchers, NLP practitioners, ML/NLP educators, and STEM students. Professionals working with text data as part of their projects will also find plenty of useful information in this book. Beginner-level familiarity with machine learning and a basic working knowledge of Python will help you get the best out of this book}, publisher={Packt Publishing Limited}, 
    author={Gazit, Lior}, 
    year={2024}, 
    language={eng} 
}

@book{Hoyle_2024, 
    address={Birmingham}, 
    edition={1}, 
    title={15 Math Concepts Every Data Scientist Should Know: Understand and learn how to apply the math behind data science algorithms}, 
    ISBN={9781837634187}, 
    abstractNote={Create more effective and powerful data science solutions by learning when, where, and how to apply key math principles that drive most data science algorithmsKey FeaturesUnderstand key data science algorithms with Python-based examplesIncrease the impact of your data science solutions by learning how to apply existing algorithmsTake your data science solutions to the next level by learning how to create new algorithmsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionData science combines the power of data with the rigor of scientific methodology, with mathematics providing the tools and frameworks for analysis, algorithm development, and deriving insights. As machine learning algorithms become increasingly complex, a solid grounding in math is crucial for data scientists. David Hoyle, with over 30 years of experience in statistical and mathematical modeling, brings unparalleled industrial expertise to this book, drawing from his work in building predictive models for the world's largest retailers. Encompassing 15 crucial concepts, this book covers a spectrum of mathematical techniques to help you understand a vast range of data science algorithms and applications. Starting with essential foundational concepts, such as random variables and probability distributions, you'll learn why data varies, and explore matrices and linear algebra to transform that data. Building upon this foundation, the book spans general intermediate concepts, such as model complexity and network analysis, as well as advanced concepts such as kernel-based learning and information theory. Each concept is illustrated with Python code snippets demonstrating their practical application to solve problems. By the end of the book, you'll have the confidence to apply key mathematical concepts to your data science challenges.What you will learnMaster foundational concepts that underpin all data science applicationsUse advanced techniques to elevate your data science proficiencyApply data science concepts to solve real-world data science challengesImplement the NumPy, SciPy, and scikit-learn concepts in PythonBuild predictive machine learning models with mathematical conceptsGain expertise in Bayesian non-parametric methods for advanced probabilistic modelingAcquire mathematical skills tailored for time-series and network data typesWho this book is forThis book is for data scientists, machine learning engineers, and data analysts who already use data science tools and libraries but want to learn more about the underlying math. Whether you're looking to build upon the math you already know, or need insights into when and how to adopt tools and libraries to your data science problem, this book is for you. Organized into essential, general, and selected concepts, this book is for both practitioners just starting out on their data science journey and experienced data scientists}, 
    publisher={Packt Publishing Limited}, 
    author={Hoyle, David}, 
    year={2024}, 
    language={eng} 
}

@article{Liu_Pan_Li_Chen_Tang_Lu_Wang_2018, 
    title={Applications of deep learning to MRI images: A survey}, 
    volume={1}, 
    ISSN={2096-0654, 2097-406X}, 
    url={https://ieeexplore.ieee.org/document/8268732/}, 
    DOI={10.26599/BDMA.2018.9020001}, 
    number={1}, 
    journal={Big Data Mining and Analytics}, 
    author={Liu, Jin and Pan, Yi and Li, Min and Chen, Ziyue and Tang, Lu and Lu, Chengqian and Wang, Jianxin}, 
    year={2018}, 
    month=mar, 
    pages={1-18} 
}
@article{Lundervold_Lundervold_2019, 
    title={An overview of deep learning in medical imaging focusing on MRI}, 
    volume={29}, 
    rights={https://www.elsevier.com/tdm/userlicense/1.0/}, 
    ISSN={09393889}, 
    url={https://linkinghub.elsevier.com/retrieve/pii/S0939388918301181}, 
    DOI={10.1016/j.zemedi.2018.11.002}, 
    number={2}, 
    journal={Zeitschrift für Medizinische Physik}, 
    author={Lundervold, Alexander Selvikvåg and Lundervold, Arvid}, 
    year={2019}, 
    month=may, 
    pages={102-127}, 
    language={en} 
}
@article{Hyun_Kim_Lee_Lee_Seo_2018, 
    title={Deep learning for undersampled MRI reconstruction}, 
    volume={63}, 
    ISSN={1361-6560}, 
    url={https://iopscience.iop.org/article/10.1088/1361-6560/aac71a}, 
    DOI={10.1088/1361-6560/aac71a}, 
    number={13}, 
    journal={Physics in Medicine and Biology}, 
    author={Hyun, Chang Min and Kim, Hwa Pyung and Lee, Sung Min and Lee, Sungchul and Seo, Jin Keun}, 
    year={2018}, 
    month=jun, 
    pages={135007} 
}

@inproceedings{Wang_Su_Ying_Peng_Zhu_Liang_Feng_Liang_2016,
    address={Prague, Czech Republic}, 
    title={Accelerating magnetic resonance imaging via deep learning}, 
    ISBN={9781479923496}, 
    url={http://ieeexplore.ieee.org/document/7493320/}, 
    DOI={10.1109/ISBI.2016.7493320}, 
    booktitle={2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)}, 
    publisher={IEEE}, 
    author={Wang, Shanshan and Su, Zhenghang and Ying, Leslie and Peng, Xi and Zhu, Shun and Liang, Feng and Feng, Dagan and Liang, Dong}, 
    year={2016}, 
    month=apr, 
    pages={514-517} 
}

@article{Hammernik_Klatzer_Kobler_Recht_Sodickson_Pock_Knoll_2018, 
    title={Learning a variational network for reconstruction of accelerated MRI data}, 
    volume={79}, 
    ISSN={0740-3194, 1522-2594}, 
    url={https://onlinelibrary.wiley.com/doi/10.1002/mrm.26977}, 
    DOI={10.1002/mrm.26977}, 
    number={6}, 
    journal={Magnetic Resonance in Medicine}, 
    author={Hammernik, Kerstin and Klatzer, Teresa and Kobler, Erich and Recht, Michael P. and Sodickson, Daniel K. and Pock, Thomas and Knoll, Florian}, 
    year={2018}, 
    month=jun, 
    pages={3055-3071}, 
    language={en} 
}

@misc{IXI_Dataset, 
    url={https://brain-development.org/ixi-dataset/}, 
    language={en-US} 
}

 @book{Raschka_2022, 
    address={Birmingham}, 
    edition={1}, 
    title={Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python}, 
    ISBN={9781801819312}, 
    publisher={Packt Publishing Limited}, 
    author={Raschka, Sebastian}, 
    year={2022}, 
    language={eng} 
}