课程信息
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第 4 门课程(共 6 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

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中级

完成时间大约为17 小时

建议:28 hours/week...

英语(English)

字幕:英语(English)
User
学习Professional Certificate的学生是
  • Data Scientists
  • Machine Learning Engineers
  • Researchers
  • Data Engineers
  • Entrepreneurs
User
学习Professional Certificate的学生是
  • Data Scientists
  • Machine Learning Engineers
  • Researchers
  • Data Engineers
  • Entrepreneurs

第 4 门课程(共 6 门)

100% 在线

立即开始,按照自己的计划学习。

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17 小时

建议:28 hours/week...

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 5 小时

Tensor and Datasets

6 个视频 (总计 44 分钟), 1 个阅读材料, 11 个测验
6 个视频
1.1 Tensors 1D13分钟
1.2 Two-Dimensional Tensors9分钟
Differentiation in PyTorch5分钟
1.3 Simple Dataset7分钟
1.5 Dataset4分钟
1 个阅读材料
Labs10分钟
5 个练习
1.1 Tensors 1D5分钟
1.2 Two-Dimensional Tensors5分钟
1.3 Derivatives in PyTorch5分钟
Simple Dataset5分钟
Datasets10分钟
2
完成时间为 2 小时

Linear Regression

7 个视频 (总计 35 分钟), 10 个测验
7 个视频
2.1 Linear Regression Training3分钟
Loss3分钟
Gradient Descent4分钟
Cost3分钟
Linear Regression PyToch5分钟
PyTorch Linear Regression Training Slope and Bias5分钟
7 个练习
Prediction in One Dimension5分钟
Linear Regression Training5分钟
Loss5分钟
Gradient Descent5分钟
Cost5分钟
Training Parameters in PyTorch5分钟
PyTorch Linear Regression Training Slope and Bias5分钟
完成时间为 3 小时

Linear Regression PyTorch Way

5 个视频 (总计 21 分钟), 8 个测验
5 个视频
Mini-Batch Gradient Descent3分钟
Optimization in PyTorch3分钟
Training, Validation and Test Split4分钟
Training, Validation and Test Split PyTorch3分钟
4 个练习
Quiz: Stochastic Gradient Descent5分钟
Mini-Batch Gradient Descent5分钟
3.3 Optimization in PyTorch5分钟
Training and Validation Data PyTorch5分钟
3
完成时间为 2 小时

Multiple Input Output Linear Regression

4 个视频 (总计 18 分钟), 6 个测验
4 个视频
Multiple Linear Regression Training2分钟
Linear Regression Multiple Outputs5分钟
Multiple Output Linear Regression Training1分钟
2 个练习
Multiple Linear Regression Prediction5分钟
Multiple Output Linear Regression5分钟
完成时间为 2 小时

Logistic Regression for Classification

4 个视频 (总计 31 分钟), 8 个测验
4 个视频
5.1 Logistic Regression: Prediction6分钟
Bernoulli Distribution and Maximum Likelihood Estimation5分钟
Logistic Regression Cross Entropy Loss10分钟
5 个练习
5.0 Linear Classifiers5分钟
5.0 Linear Classifiers5分钟
5.1 Logistic Regression: Prediction10分钟
Bernoulli Distribution and Maximum Likelihood Estimation5分钟
5.3 Logistic Regression Cross Entropy Loss10分钟
4
完成时间为 2 小时

Softmax Rergresstion

3 个视频 (总计 18 分钟), 5 个测验
3 个视频
6.2 Softmax Function:Using Lines to Classify Data3分钟
Softmax PyTorch6分钟
3 个练习
6.1 Softmax Function:Using Lines to Classify Data5分钟
6.2 Softmax Prediction5分钟
6.3 Softmax PyTorch Quizz5分钟
完成时间为 3 小时

Shallow Neural Networks

6 个视频 (总计 33 分钟), 12 个测验
6 个视频
More Hidden Neurons2分钟
Neural Networks with Multiple Dimensional Input5分钟
7.4 Multi-Class Neural Networks5分钟
7.5 Backpropagation5分钟
7.5 Activation Functions4分钟
6 个练习
Neural Networks5分钟
More Hidden Neurons 5分钟
Neural Networks with Multiple Dimensional Inputs5分钟
Multi-Class Neural Networks5分钟
Backpropagation5分钟
Activation Functions5分钟

讲师

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Joseph Santarcangelo

Ph.D., Data Scientist at IBM
IBM Developer Skills Network

关于 IBM

IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame....

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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