Arama Sonuçları - (((( loss layers training . ) OR ( loss layers training . ))) OR ( class layer training . ))~

Deep Learning with Python, Second Edition. Yazar: Chollet, Francois

İçindekiler: “…-- 3.3 Keras and TensorFlow: A brief history -- 3.4 Setting up a deep learning workspace -- 3.4.1 Jupyter notebooks: The preferred way to run deep learning experiments -- 3.4.2 Using Colaboratory -- 3.5 First steps with TensorFlow -- 3.5.1 Constant tensors and variables -- 3.5.2 Tensor operations: Doing math in TensorFlow -- 3.5.3 A second look at the GradientTape API -- 3.5.4 An end-to-end example: A linear classifier in pure TensorFlow -- 3.6 Anatomy of a neural network: Understanding core Keras APIs -- 3.6.1 Layers: The building blocks of deep learning -- 3.6.2 From layers to models -- 3.6.3 The "compile" step: Configuring the learning process -- 3.6.4 Picking a loss function -- 3.6.5 Understanding the fit() method -- 3.6.6 Monitoring loss and metrics on validation data -- 3.6.7 Inference: Using a model after training -- Summary -- 4 Getting started with neural networks: Classification and regression -- 4.1 Classifying movie reviews: A binary classification example -- 4.1.1 The IMDB dataset -- 4.1.2 Preparing the data -- 4.1.3 Building your model -- 4.1.4 Validating your approach.…”
Full-text access
OPAC'ta görüntüle

Deep Learning : Foundations and Concepts. Yazar: Bishop, Christopher M.

İçindekiler: “…The Bias-Variance Trade-off -- Exercises -- 5 Single-layer Networks: Classification -- 5.1. Discriminant Functions -- 5.1.1 Two classes -- 5.1.2 Multiple classes -- 5.1.3 1-of-K coding -- 5.1.4 Least squares for classification -- 5.2. …”
Full-text access
OPAC'ta görüntüle

Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications FICTA 2016, Volume 1 /

İçindekiler: “…Minimization of Energy Consumption Using X-Layer Network Transformation Model for IEEE 802.15.4-Based MWSNs -- Chapter 75. …”
Full-text access
OPAC'ta görüntüle