Tensorflow 2.0:深度学习和人工智能视频教程

机器学习&计算机视觉的神经网络，时间序列分析，NLP，GANs，强化学习，+更多！

你会学到什么
人工神经网络/深度神经网络
预测股票收益
时间数列预测法
计算机视觉
如何构建深度强化学习炒股机器人
生成对抗网络
推荐系统
图像识别
卷积神经网络
递归神经网络
使用Tensorflow通过RESTful API为您的模型提供服务
使用Tensorflow Lite为移动(Android、iOS)和嵌入式设备导出您的模型
使用Tensorflow的分布策略来并行学习
低级张量流，渐变带，以及如何构建自己的定制模型
具有深度学习的自然语言处理
用代码演示摩尔定律
迁移学习创建最先进的图像分类器

类型:电子教学| MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz
语言：英语+中英文字幕（云桥CG资源站 机译）|大小:6.83 GB |时长:22h 8m


要求
知道如何用Python和Numpy编码
对于理论部分(可选)，理解导数和概率

描述
欢迎使用Tensorflow 2.0！

多么激动人心的时刻。距离Tensorflow发布已经将近4年了，库已经进化到了正式的第二个版本。

Tensorflow是谷歌的深度学习和人工智能库。

深度学习最近取得了一些惊人的成就，例如

生成从未存在过的人和事物的美丽、照片般逼真的图像(GANs)

在战略游戏围棋中击败世界冠军，以及复杂的视频游戏，如CS:GO和Dota 2(深度强化学习)

自动驾驶汽车(计算机视觉)

语音识别(例如Siri)和机器翻译(自然语言处理)

甚至制作人们做和说他们从未做过的事情的视频(deep fakes——深度学习的一个潜在邪恶应用)



Tensorflow是世界上最受欢迎的深度学习库，它是由谷歌建立的，谷歌的母公司Alphabet最近成为世界上现金最丰富的公司(就在我写这篇文章的几天前)。它是许多做人工智能和机器学习的公司的首选库。

换句话说，如果你想做深度学习，你必须知道张量流。

这门课程是为从初级到专家级的学生开设的。

如果你刚刚参加了我的免费Numpy先决条件，那么你就知道你需要的一切了。我们将从一些非常基本的机器学习模型开始，并推进到最先进的概念。

在此过程中，您将了解所有主要的深度学习架构，如深度神经网络、卷积神经网络(图像处理)和递归神经网络(序列数据)。Tensorflow 2.0: Deep Learning and Artificial Intelligence

目前的项目包括

自然语言处理

推荐系统

计算机视觉的迁移学习

生成对抗网络

深度强化学习炒股机器人

即使你已经参加了我之前的所有课程，你仍然会学习如何转换你之前的代码，以便它使用Tensorflow 2.0，并且在这门课程中有全新的和从未见过的项目，例如时间序列预测和如何进行股票预测。

这门课程是为想快速学习的学生设计的，但也有“深入”部分，以防你想更深入地挖掘理论(如什么是损失函数，以及梯度下降方法的不同类型)。


高级Tensorflow主题包括

部署具有Tensorflow服务的模型(云中的Tensorflow)

使用Tensorflow Lite部署模型(移动和嵌入式应用程序)

具有分布策略的分布式张量流训练

编写您自己的自定义张量流模型

将Tensorflow 1.x代码转换为Tensorflow 2.0

常数、变量和张量

急切的执行

梯度带

教员备注:本课程侧重于广度而非深度，较少的理论有利于构建更酷的东西。如果你正在寻找一个更理论密集的课程，这不是它。一般来说，对于这些主题中的每一个(推荐系统、自然语言处理、强化学习、计算机视觉、GANs等。)我已经开设了专门针对这些主题的课程。

感谢阅读，课堂上见！

我应该按什么顺序上你的课？

查看讲座“机器学习和人工智能先决条件路线图”(可在我的任何课程的常见问题中找到，包括免费的Numpy课程)

这门课是给谁的
希望在Tensorflow 2.0中了解深度学习和人工智能的初学者到高级学生


Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 6.83 GB | Duration: 22h 8m

Machine Learning & Neural Networks for Computer Vision, Time Series Analysis, NLP, GANs, Reinforcement Learning, +More!

What you’ll learn
Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)
Predict Stock Returns
Time Series Forecasting
Computer Vision
How to build a Deep Reinforcement Learning Stock Trading Bot
GANs (Generative Adversarial Networks)
Recommender Systems
Image Recognition
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Use Tensorflow Serving to serve your model using a RESTful API
Use Tensorflow Lite to export your model for mobile (Android, iOS) and embedded devices
Use Tensorflow’s Distribution Strategies to parallelize learning
Low-level Tensorflow, gradient tape, and how to build your own custom models
Natural Language Processing (NLP) with Deep Learning
Demonstrate Moore’s Law using Code
Transfer Learning to create state-of-the-art image classifiers

Requirements
Know how to code in Python and Numpy
For the theoretical parts (optional), understand derivatives and probability

Description
Welcome to Tensorflow 2.0!

What an exciting time. It’s been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version.

Tensorflow is Google’s library for deep learning and artificial intelligence.

Deep Learning has been responsible for some amazing achievements recently, such as

Generating beautiful, photo-realistic images of people and things that never existed (GANs)

Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)

Self-driving cars (Computer Vision)

Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)

Even creating videos of people doing and saying things they never did (DeepFakes – a potentially nefarious application of deep learning)

Tensorflow is the world’s most popular library for deep learning, and it’s built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning.

In other words, if you want to do deep learning, you gotta know Tensorflow.

This course is for beginner-level students all the way up to expert-level students. How can this be?

If you’ve just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts.

Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data).

Current projects include

Natural Language Processing (NLP)

Recommender Systems

Transfer Learning for Computer Vision

Generative Adversarial Networks (GANs)

Deep Reinforcement Learning Stock Trading Bot

Even if you’ve taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses Tensorflow 2.0, and there are all-new and never-before-seen projects in this course such as time series forecasting and how to do stock predictions.

This course is designed for students who want to learn fast, but there are also “in-depth” sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches).

Advanced Tensorflow topics include

Deploying a model with Tensorflow Serving (Tensorflow in the cloud)

Deploying a model with Tensorflow Lite (mobile and embedded applications)

Distributed Tensorflow training with Distribution Strategies

Writing your own custom Tensorflow model

Converting Tensorflow 1.x code to Tensorflow 2.0

Constants, Variables, and Tensors

Eager execution

Gradient tape

Instructor’s Note: This course focuses on breadth rather than depth, with less theory in favor of building more cool stuff. If you are looking for a more theory-dense course, this is not it. Generally, for each of these topics (recommender systems, natural language processing, reinforcement learning, computer vision, GANs, etc.) I already have courses singularly focused on those topics.

Thanks for reading, and I’ll see you in class!

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?

Check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in the FAQ of any of my courses, including the free Numpy course)

Who this course is for
Beginners to advanced students who want to learn about deep learning and AI in Tensorflow 2.0
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