📕 Paradigms of artificial intelligence programming (1991)
📕 Artificial intelligence a modern approach (1994)
📕 Machine learning (1997)
📖 The quest for artificial intelligence – a history of ideas and achievements (2009)
📕 Introduction to artificial intelligence (2011)
📕 Machine learning: a probabilistic perspective (2012)
📖 The Nature of Code (2012)
📕 Superintelligence: paths, dangers, strategies (2014)
📖 Understanding machine learning: from theory to algorithms (2014)
📖 Neural Networks and Deep Learning (2015)
📕 Deep Larning with Python (2017)
📕 Tensorflow machine learning cookbook (2017)
Code
📕 Hands-On Machine Learning with Scikit-Learn and TensorFlow (2017)
📕 Machine Learning with Go (2017) – Build simple, maintainable, and easy to deploy machine learning applications.
📖 Interpretable Machine Learning (2018)
📖 Deep learning
📖 Interpretable machine learning (2018) – Explaining the decisions and behavior of machine learning models.
📕 How Machine Learning Works (2019)- An introduction to both ML’s practice and math foundations in a non-threatning approach.
Code
📕 Grokking Deep Learning (2019)
📕 MachineLearningWithTensorFlow2ed (2020) – Book on general purpose machine learning techniques regression, classification, unsupervised clustering, reinforcement learning, auto encoders, convolutional neural networks, RNNs, LSTMs, using TensorFlow 1.14.1.
📕 Machine Learning Bookcamp (2020) – Project-based approach to learning machine learning.
📕 Trust in Machine Learning (2021) – Book about how to build machine learning systems that are explainable, robust, transparent, and optimized for fairness.
📖 Distributed Machine Learning Patterns (2023) – Distributed machine learning system patterns.