Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By : Antonio Gulli, Amita Kapoor, Sujit Pal
Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By: Antonio Gulli, Amita Kapoor, Sujit Pal

Overview of this book

Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.
Table of Contents (19 chapters)
17
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18
Index

References

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  2. Mikolov, T., et al. (2013, Sep 17). Exploiting Similarities among Languages for Machine Translation. arXiv:1309.4168v1 [cs.CL].
  3. Mikolov, T., et al. (2013). Distributed Representations of Words and Phrases and their Compositionality. Advances in Neural Information Processing Systems 26 (NIPS 2013).
  4. Pennington, J., Socher, R., Manning, C. (2014). GloVe: Global Vectors for Word Representation. D14-1162, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP).
  5. Niu, F., et al (2011, 11 Nov). HOGWILD! A Lock-Free Approach to Parallelizing Stochastic Gradient Descent. arXiv:1106.5730v2 [math.OC].
  6. Levy, O., Goldberg, Y. (2014). Neural Word Embedding as Implicit Matrix Factorization. Advances in Neural Information Processing Systems 27 (NIPS 2014).
  7. Mahoney, M. (2011, 1 Sep). text8 dataset. http...