My all-round favourite for ML-in-Finance: "Advances in Financial Machine Learning" by M. de Prado (https://www.wiley.com/en-hk/Advances+in+Financial+Machine+Learning-p-9781119482086)
He doesn't spend so much time dealing with specific ML models, but talks about the unique challenges faced by financial data scientists and the ways to handle data, conduct strategy research and backtesting, and the style and techniques (labelling, bagging, metamodels...) to apply generic ML techniques in a financial setting.
My favourite introduction to Deep Learning: Hands-On Machine Learning with SciKit-Learn and TensorFlow
Intensive introduction to Neural Nets via Keras (the author created this library) and Tensorflow, with lots of Python code examples
My favourite pure-programming for Data Science-type problems: "Scala for Data Science" by P Bugnion
An immersive introduction to Scala with a focus on Data Science problems, and a focus on how to move products into production safely