Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with PythonApress, 2019 M12 17 - 164 páginas Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways. What You'll LearnReview the new features of TensorFlow 2.0Use TensorFlow 2.0 to build machine learning and deep learning models Perform sequence predictions using TensorFlow 2.0Deploy TensorFlow 2.0 models with practical examples Who This Book Is For Data scientists, machine and deep learning engineers. |
Contenido
Introduction to TensorFlow 20 | 1 |
Supervised Learning with TensorFlow | 25 |
Neural Networks and Deep Learning with TensorFlow | 53 |
Images with TensorFlow | 75 |
Natural Language Processing with TensorFlow 20 | 107 |
TensorFlow Models in Production | 131 |
160 | |
Otras ediciones - Ver todas
Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models ... Pramod Singh,Avinash Manure Sin vista previa disponible - 2020 |
Términos y frases comunes
activation_function algorithms APIs application architecture array artificial neural network autoencoders basis vectors build chapter cluster computation computer vision convolutional kernels convolutional neural network Databricks Dataset Labels Dataset Shape deep learning deep neural network deploy eager execution example Fashion-MNIST data set Flask function Google Colab environment graph hidden layer Images Dataset implementation using TensorFlow import numpy import tensorflow inplace=True input data Keras Kubeflow Learn TensorFlow 2.0 linear regression load logistic regression loss function machine learning model MIT License model deployment neurons new_image nodes Notebook Servers numpy as np optimizer output layer parameters and note pixels Pramod Singh prediction print('No print('Test Data print('Training Data print(tf.__version__ Python ReLU ResNet shown in Figure simple neural network softmax supervised learning supervised machine learning Task/Domain techniques tensor tensorflow as tf tensorflow import keras test_images test_labels tokenization training_ training_images training_labels transfer learning users variables version of TensorFlow word embeddings