Arama Sonuçları - The Deeper Meaning of Liff~

Deep Learning with TensorFlow and Keras - 3rd Edition : Build and Deploy Supervised, Unsupervised, Deep, and Reinforcement Learning Models. Yazar: Kapoor, Amita

İçindekiler: “…Named entity recognition -- Summarization -- Fine-tuning -- TFHub -- Evaluation -- Quality -- GLUE -- SuperGLUE -- SQuAD -- RACE -- NLP-progress -- Size -- Larger doesn't always mean better -- Cost of serving -- Optimization -- Quantization -- Weight pruning -- Distillation -- Common pitfalls: dos and don'ts -- Dos -- Don'ts -- The future of transformers -- Summary -- Chapter 7: Unsupervised Learning -- Principal component analysis -- PCA on the MNIST dataset -- TensorFlow Embedding API -- K-means clustering -- K-means in TensorFlow -- Variations in k-means -- Self-organizing maps -- Colour mapping using a SOM -- Restricted Boltzmann machines -- Reconstructing images using an RBM -- Deep belief networks -- Summary -- References -- Chapter 8: Autoencoders -- Introduction to autoencoders -- Vanilla autoencoders -- TensorFlow Keras layers ‒ defining custom layers -- Reconstructing handwritten digits using an autoencoder -- Sparse autoencoder -- Denoising autoencoders -- Clearing images using a denoising autoencoder -- Stacked autoencoder -- Convolutional autoencoder for removing noise from images -- A TensorFlow Keras autoencoder example ‒ sentence vectors -- Variational autoencoders -- Summary -- References -- Chapter 9: Generative Models -- What is a GAN? …”
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8th RILEM International Conference on Mechanisms of Cracking and Debonding in Pavements

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