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 reinforcement learning with tf-agent models. With this book, you’ll delve into applied deep learning practical functions and build a wealth of knowledge about how to use TensorFlow effectively.
What You'll Learn

• Compare competing technologies and see why TensorFlow is more popular
  
• Generate text, image, or sound with GANs
  
• Predict the rating or preference a user will give to an item
  
• Sequence data with recurrent neural networks

Who This Book Is For
Data scientists and programmers new to the fields of deep learning and machine learning APIs.

使用TensorFlow实现深度学习应用，同时通过深入的概念解释学习 "为什么"。

你将从学习深度学习比其他机器学习模型提供的东西开始。然后熟悉用于创建深度学习模型的几种技术。虽然其中一些技术是互补的，如Pandas、Scikit-Learn和Numpy，但其他技术是竞争对手，如PyTorch、Caffe和Theano。本书澄清了深度学习和Tensorflow在其同行中的地位。

然后，你将在有监督的深度学习模型上工作，获得该技术的应用经验。在把它变成深度神经网络之前，将使用一个单层的多感知器来建立一个浅层神经网络。在展示了ANN的结构后，将用Tensorflow 2.0 Keras API创建一个真实的应用程序。接下来，你将研究数据增量和批量规范化方法。然后，时尚的MNIST数据集将被用来训练一个CNN。CIFAR10和Imagenet预训练的模型将被加载，以创建已经先进的CNN。

最后，进入理论应用和使用自动编码器的无监督学习以及使用tf-agent模型的强化学习。通过这本书，你将深入研究应用深度学习的实用功能，并建立关于如何有效使用TensorFlow的丰富知识。

你会学到什么

比较竞争的技术，看看为什么TensorFlow更受欢迎

用GANs生成文本、图像或声音

预测用户对一个项目的评价或偏好

用递归神经网络对数据进行排序

本书适用对象

刚接触深度学习和机器学习API领域的数据科学家和程序员。

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