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所在平台: Udemy |
课程主页: https://www.udemy.com/course/tensorflow-2/
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课程名称：使用Keras API的TensorFlow 2.0完整指南 课程概述：欢迎来到TensorFlow 2.0！TensorFlow 2.0引入了许多简化模型开发和维护过程的新特性，使得复杂概念更易于理解。这门课程将帮助您掌握深度学习这一人工智能领域的快速发展方向，展示深度学习模型如何解决复杂任务。 课程结构分为五个部分： 第一部分：介绍我们将使用的技术栈和TensorFlow 2.0库的基础知识。 第二部分：深入深度学习的世界，您将实现多种类型的神经网络，包括全连接神经网络、卷积神经网络和递归神经网络，最后构建一个在狗与猫数据集上实现顶尖结果的迁移学习应用。 第三部分：学习使用强化学习创建股票市场交易机器人，特别是深度Q网络的实现。 第四部分：关于TensorFlow扩展（TFX）的学习，您将掌握如何处理数据并创建生产使用的数据管道，包括使用TensorFlow数据验证库检查数据集中的异常情况，以及使用TensorFlow转换库创建数据预处理管道。此外，您还将利用Flask Python库和预训练模型创建自己的时尚API。 第五部分：学习如何将训练分配到多个GPU或服务器上，实现更高效的训练过程。 通过本课程，您将全面掌握TensorFlow 2.0及深度学习的应用，适合希望在人工智能领域深入发展的学习者。
Welcome to Tensorflow 2.0!TensorFlow 2.0 has just been released, and it introduced many features that simplify the model development and maintenance processes. From the educational side, it boosts people's understanding by simplifying many complex concepts. From the industry point of view, models are much easier to understand, maintain, and develop.Deep Learning is one of the fastest growing areas of Artificial Intelligence. In the past few years, we have proven that Deep Learning models, even the simplest ones, can solve very hard and complex tasks. Now, that the buzz-word period of Deep Learning has, partially, passed, people are releasing its power and potential for their product improvements.The course is structured in a way to cover all topics from neural network modeling and training to put it in production.In Part 1 of the course, you will learn about the technology stack that we will use throughout the course (Section 1) and the TensorFlow 2.0 library basics and syntax (Section 2).In Part 2 of the course, we will dig into the exciting world of deep learning. Through this part of the course, you will implement several types of neural networks (Fully Connected Neural Network (Section 3), Convolutional Neural Network (Section 4), Recurrent Neural Network (Section 5)). At the end of this part, Section 6, you will learn and build their own Transfer Learning application that achieves state of the art (SOTA) results on the Dogs vs. Cats dataset.After passing the part 2 of the course and ultimately learning how to implement neural networks, in Part 3 of the course, you will learn how to make your own Stock Market trading bot using Reinforcement Learning, specifically Deep-Q Network.Part 4 is all about TensorFlow Extended (TFX). In this part of the course, you will learn how to work with data and create your own data pipelines for production. In Section 8 we will check if the dataset has any anomalies using the TensorFlow Data Validation library and after learn how to check a dataset for anomalies, in Section 9, we will make our own data preprocessing pipeline using the TensorFlow Transform library.In Section 10 of the course, you will learn and create your own Fashion API using the Flask Python library and a pre-trained model. Throughout this section, you will get a better picture of how to send a request to a model over the internet. However, at this stage, the architecture around the model is not scalable to millions of request. Enter the Section 11. In this section of the course, you will learn how to improve solution from the previous section by using the TensorFlow Serving library. In a very easy way, you will learn and create your own Image Classification API that can support millions of requests per day!These days it is becoming more and more popular to have a Deep Learning model inside an Android or iOS application, but neural networks require a lot of power and resources! That's where the TensorFlow Lite library comes into play. In Section 12 of the course, you will learn how to optimize and convert any neural network to be suitable for a mobile device.To conclude with the learning process and the Part 5 of the course, in Section 13 you will learn how to distribute the training of any Neural Network to multiple GPUs or even Servers using the TensorFlow 2.0 library.
