My Suggested Reading List

Category 1: Generative AI

• Ian Goodfellow et al. (2014). “Generative Adversarial Nets.” Advances in Neural Information Processing Systems (NeurIPS).

  The seminal paper that introduced GANs, laying the foundation for a myriad of generative models and approaches to producing synthetic data.

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• Diederik P. Kingma & Max Welling (2013). “Auto-Encoding Variational Bayes.”

  Introduced the concept of VAEs, a cornerstone generative framework that combines probabilistic modeling with neural networks, widely used for dimensionality reduction, anomaly detection, and synthetic data generation.

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• Vaswani, Ashish et al. (2017): "Attention is All You Need."

  Introduced the Transformer architecture, which has become the foundation for modern NLP systems like BERT and GPT.

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• Volodymyr Mnih et al. (2015): "Human-level control through deep reinforcement learning."

  Introduced Deep Q-Learning, demonstrating human-level performance on Atari games.

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• Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin (2016): "Why Should I Trust You? Explaining the Predictions of Any Classifier."

  Proposed LIME, a method for explaining the predictions of machine learning models.

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• Yann LeCun et al. (1998): "Gradient-based learning applied to document recognition."

  Introduced convolutional neural networks (CNNs) and their application to handwritten digit recognition.

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• Cortes, Corinna, and Vladimir Vapnik (1995): "Support-vector networks."

  Introduced the concept of SVMs, a widely used algorithm for classification and regression tasks.

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• Leo Breiman (2001): "Random Forests."

  Introduced the Random Forest algorithm, an ensemble method combining bagging and decision trees.

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Category 2: Machine Learning

• Muller and Guido (2017): Introduction to Machine Learning With Python (Ebook)

  Introduction to machine learning concepts and applications using Python.

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• Ayodele, Taiwo Oladipupo (2016): "Machine learning overview."

  An overview of machine learning concepts and their applications.

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• Mitchell, Tom. M. (2017): "Key Ideas in Machine Learning"

  Exploration of foundational concepts in machine learning, including key ideas and methodologies.

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• Rudin, Cynthia (2019): "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead."

  Discussion on the importance of interpretable models over black-box methods in critical applications.

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• Dietterich, Thomas G. (2002): "Ensemble Learning."

  An overview of ensemble methods, including bagging, boosting, and their practical applications.

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• Hastie, Trevor, Robert Tibshirani, and Jerome Friedman (2016): Chapters 3, 5, 9, and 10

  Advanced topics in data mining and machine learning, covering linear models, ensemble methods, and more.

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• Khurana, D., Koli, A., Khatter, K., & Singh, S. (2017): "Natural language processing: State of the art, current trends and challenges."

  A comprehensive review of NLP techniques, challenges, and trends.

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• Blei, David M., and John D. Lafferty (2006): "Dynamic topic models."

  An introduction to dynamic topic modeling, its methodology, and applications in machine learning.

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Category 3: Data Network Design

Category 4: Programming

Category 5: General