利用TensorFlow进行深度学习

Deep Learning with Tensorflow

Much of the world’s data is unstructured. Think images, sound, and textual data. Learn how to apply Deep Learning with TensorFlow to this type of data to solve real-world problems.

612 次查看
IBM
edX
  • 完成时间大约为 5
  • 中级
  • 英语
注:因开课平台的各种因素变化,以上开课日期仅供参考

你将学到什么

Explain foundational TensorFlow concepts such as the main functions, operations and the execution pipelines.

Describe how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions.

Understand different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

Apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained.

课程概况

Traditional neural networks rely on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kind of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which consitutes the vast majority of data in the world.

TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

In this TensorFlow course, you will learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions.

This concept is then explored in the Deep Learning world. You will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

课程大纲

Module 1 – Introduction to TensorFlow* HelloWorld with TensorFlow* Linear Regression* Nonlinear Regression* Logistic RegressionModule 2 – Convolutional Neural Networks (CNN)* CNN Application* Understanding CNNsModule 3 – Recurrent Neural Networks (RNN)* Intro to RNN Model* Long Short-Term memory (LSTM)Module 4 - Restricted Boltzmann Machine* Restricted Boltzmann Machine* Collaborative Filtering with RBMModule 5 - Autoencoders* Introduction to Autoencoders and Applications* Autoencoders* Deep Belief Network

预备知识

Python & Jupyter notebooks
Machine Learning concepts
Deep Learning concepts

千万首歌曲。全无广告干扰。
此外,您还能在所有设备上欣赏您的整个音乐资料库。免费畅听 3 个月,之后每月只需 ¥10.00。
Apple 广告
声明:MOOC中国十分重视知识产权问题,我们发布之课程均源自下列机构,版权均归其所有,本站仅作报道收录并尊重其著作权益。感谢他们对MOOC事业做出的贡献!
  • Coursera
  • edX
  • OpenLearning
  • FutureLearn
  • iversity
  • Udacity
  • NovoEd
  • Canvas
  • Open2Study
  • Google
  • ewant
  • FUN
  • IOC-Athlete-MOOC
  • World-Science-U
  • Codecademy
  • CourseSites
  • opencourseworld
  • ShareCourse
  • gacco
  • MiriadaX
  • JANUX
  • openhpi
  • Stanford-Open-Edx
  • 网易云课堂
  • 中国大学MOOC
  • 学堂在线
  • 顶你学堂
  • 华文慕课
  • 好大学在线CnMooc
  • (部分课程由Coursera、Udemy、Linkshare共同提供)

© 2008-2022 CMOOC.COM 慕课改变你,你改变世界