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    • Riesen Kaspar
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    Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow, 2nd Edition
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    • Sudharsan Ravichandiran
    • Packt Publishing
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    Perform accessible machine learning and extreme gradient boosting with Python
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    • Corey Wade; Kevin Glynn
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    • Engineering Science Reference
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    Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch
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    • Maxime Labonne
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    Over 70 recipes for building AI solutions for smart homes, industrial IoT, and smart cities
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    • Michael Roshak
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    Proceedings of the IFIP TC6/WG6.1. Twelfth International Symposium on Protocol Specification, Testing and Verification, Lake Buena Vista, Florida, U.S.A., 22-25 June, 1992
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    • Sara Moein
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    Computer Vision and Natural Language Processing
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    • Raj, Pethuru
    • Auerbach Publications (T&F)
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    A Practical Project-Driven Introduction to Machine Learning
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    • Gwendolyn Stripling; Michael Abel
    • O'Reilly Media
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    The ultimate guide to using Python to explore the true power of neural networks through six projects
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    • James Loy
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    Build high-performing artificial neural network architectures using neuroevolution-based algorithms
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    • Iaroslav Omelianenko
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    A Production-First Approach
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    • Yaron Haviv; Noah Gift
    • O'Reilly Media
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    • Frances Buontempo
    • Pragmatic Bookshelf
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    Rapidly build practical online machine learning solutions using River and other top key frameworks
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    • Joos Korstanje
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    A practical guide to performing effective queries, data visualization, and reporting techniques
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    • Harish Gulati
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    Improve your data mining capabilities with advanced predictive modeling
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    • Jesus Salcedo
    • Packt Publishing
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