| 期刊全称 | Applied Neural Networks with TensorFlow 2 | | 期刊简称 | API Oriented Deep Le | | 影响因子2023 | Orhan Gazi Yalçın | | 视频video | | | 发行地址 | Differentiate supervised, unsupervised, and reinforcement machine learning.Serve trained deep learning models on the web with the Flask lightweight framework.Build a shallow neural network | | 图书封面 |  | | 影响因子 | Implement deep learning applications using TensorFlow while learning the “why” through in-depth conceptual explanations. .You’ll start by learning what deep learning offers over other machine learning models. Then familiarize yourself with several technologies used to create deep learning models. While some of these technologies are complementary, such as Pandas, Scikit-Learn, and Numpy—others are competitors, such as PyTorch, Caffe, and Theano. This book clarifies the positions of deep learning and Tensorflow among their peers. .You‘ll then work on supervised deep learning models to gain applied experience with the technology. A single-layer of multiple perceptrons will be used to build a shallow neural network before turning it into a deep neural network. After showing the structure of the ANNs, a real-life application will be created with Tensorflow 2.0 Keras API. Next, you’ll work on data augmentation and batch normalization methods. Then, the Fashion MNIST dataset will be used to train a CNN. CIFAR10 and Imagenet pre-trained models will be loaded to create already advanced CNNs.. Finally, move into theoretical applications and unsupervised learning with auto-encoders and rein | | Pindex | Book 2021 |
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发表于 2025-3-21 19:28:55
