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第 5 门课程(共 6 门)

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中级

完成时间大约为8 小时

建议:4-5 hours/week...

英语(English)

字幕:英语(English)

100% 在线

立即开始,按照自己的计划学习。

第 5 门课程(共 6 门)

可灵活调整截止日期

根据您的日程表重置截止日期。

中级

完成时间大约为8 小时

建议:4-5 hours/week...

英语(English)

字幕:英语(English)

讲师

授课教师评分4.4/5 (5 个评分)Info
授课教师 Alex Aklson 的图片

Alex Aklson 

Ph.D., Data Scientist
IBM Developer Skills Network
193,068 个学生
7 门课程

提供方

IBM 徽标

IBM

教学大纲 - 您将从这门课程中学到什么

1

1

完成时间为 4 小时

Introduction

完成时间为 4 小时
4 个视频 (总计 24 分钟), 1 个阅读材料, 4 个测验
4 个视频
Welcome2分钟
Introduction to TensorFlow7分钟
Introduction to Deep Learning2分钟
Deep Neural Networks11分钟
1 个阅读材料
Syllabus10分钟
1 个练习
Deep Neural Networks and TensorFlow30分钟
2

2

完成时间为 3 小时

Supervised Learning Models

完成时间为 3 小时
3 个视频 (总计 22 分钟)
3 个视频
Convolutional Neural Networks (CNNs) for Classification4分钟
Convolutional Neural Networks (CNNs) Architecture13分钟
1 个练习
Convolutional Neural Networks30分钟
3

3

完成时间为 3 小时

Supervised Learning Models (Cont'd)

完成时间为 3 小时
4 个视频 (总计 22 分钟)
4 个视频
Recurrent Neural Networks (RNNs)5分钟
The Long Short Term Memory (LSTM) Model5分钟
Language Modelling7分钟
1 个练习
Recurrent Neural Networks30分钟
4

4

完成时间为 3 小时

Unsupervised Deep Learning Models

完成时间为 3 小时
2 个视频 (总计 10 分钟)
2 个视频
Restricted Boltzmann Machines (RBMs)5分钟
1 个练习
Restricted Boltzmann Machines30分钟
4.2
29 条评论Chevron Right

来自Building Deep Learning Models with TensorFlow的热门评论

创建者 LDNov 6th 2019

course needed to be updated for labs. Now Google moved to Tensorflow 2.0 this year.

创建者 SAFeb 4th 2020

It helped me to understand how TensorFlow can be used to build the neural networks

关于 IBM AI Engineering 专业证书

The rapid pace of innovation in Artificial Intelligence (AI) is creating enormous opportunity for transforming entire industries and our very existence. After competing this comprehensive 6 course Professional Certificate, you will get a practical understanding of Machine Learning and Deep Learning. You will master fundamental concepts of Machine Learning and Deep Learning, including supervised and unsupervised learning. You will utilize popular Machine Learning and Deep Learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow applied to industry problems involving object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers. You will be able to scale Machine Learning on Big Data using Apache Spark. You will build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders. By the end of this Professional Certificate, you will have completed several projects showcasing your proficiency in Machine Learning and Deep Learning, and become armed with skills for a career as an AI Engineer....
IBM AI Engineering

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