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Deep Learning with Python, Second Edition provides the student a detailed overview of Python concepts, including learning rules and representations from data, the modern machine learning landscape, the democratization of deep learning, rank-3 and higher-rank tensors, element-wise operations, stochastic gradient descent, monitoring loss and metrics on validation data, using a trained model to generate predictions on new data, among others. This textbook is direct and concise with systematic content designed to engage today’s student through visually engaging content. There is a wide variety of assignment types that help students master Python concepts and build real-world working capability. Deep Learning with Python, Second Edition goes into the Boston housing price dataset, the importance of having sufficiently large intermediate layers, tuning key gradient descent parameters, interpreting what convnets learn, advanced use of recurrent neural networks, the max-pooling operation, mixing and matching different components to build Keras models, implementing text generation with Keras, a schematic GAN implementation, and blending together deep learning and program synthesis. A chapter is broken down into setting the course toward greater generality in AI. Buy Deep Learning with Python, Second Edition and be confident in your Python education and career.
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