The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch's tensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression. Followed by Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers. Then Convolutional Neural Networks and Transfer learning will be covered. Finally, several other Deep learning methods will be covered.
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課程信息
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第 4 門課程（共 6 門）
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中級
完成時間大約為30 小時
英語（English）
字幕：英語（English）
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 4 門課程（共 6 門）
可靈活調整截止日期
根據您的日程表重置截止日期。
中級
完成時間大約為30 小時
英語（English）
字幕：英語（English）
提供方
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.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 5 小時
Tensor and Datasets
完成時間為 5 小時
6 個視頻 （總計 44 分鐘）, 1 個閱讀材料, 11 個測驗
6 個視頻
1.1 Tensors 1D13分鐘
1.2 Two-Dimensional Tensors9分鐘
Differentiation in PyTorch5分鐘
1.3 Simple Dataset7分鐘
1.5 Dataset4分鐘
1 個閱讀材料
Labs10分鐘
5 個練習
1.1 Tensors 1D5分鐘
1.2 Two-Dimensional Tensors5分鐘
1.3 Derivatives in PyTorch5分鐘
Simple Dataset5分鐘
Datasets10分鐘
完成時間為 2 小時
Linear Regression
完成時間為 2 小時
7 個視頻 （總計 35 分鐘）
7 個視頻
2.1 Linear Regression Training3分鐘
Loss3分鐘
Gradient Descent4分鐘
Cost3分鐘
Linear Regression PyToch5分鐘
PyTorch Linear Regression Training Slope and Bias5分鐘
7 個練習
Prediction in One Dimension5分鐘
Linear Regression Training5分鐘
Loss5分鐘
Gradient Descent5分鐘
Cost5分鐘
Training Parameters in PyTorch5分鐘
PyTorch Linear Regression Training Slope and Bias5分鐘
完成時間為 3 小時
Linear Regression PyTorch Way
完成時間為 3 小時
5 個視頻 （總計 21 分鐘）
5 個視頻
Mini-Batch Gradient Descent3分鐘
Optimization in PyTorch3分鐘
Training, Validation and Test Split4分鐘
Training, Validation and Test Split PyTorch3分鐘
4 個練習
Quiz: Stochastic Gradient Descent5分鐘
Mini-Batch Gradient Descent5分鐘
3.3 Optimization in PyTorch5分鐘
Training and Validation Data PyTorch5分鐘
完成時間為 2 小時
Multiple Input Output Linear Regression
完成時間為 2 小時
4 個視頻 （總計 18 分鐘）
4 個視頻
Multiple Linear Regression Training2分鐘
Linear Regression Multiple Outputs5分鐘
Multiple Output Linear Regression Training1分鐘
2 個練習
Multiple Linear Regression Prediction5分鐘
Multiple Output Linear Regression5分鐘
完成時間為 2 小時
Logistic Regression for Classification
完成時間為 2 小時
4 個視頻 （總計 31 分鐘）
4 個視頻
5.1 Logistic Regression: Prediction6分鐘
Bernoulli Distribution and Maximum Likelihood Estimation5分鐘
Logistic Regression Cross Entropy Loss10分鐘
5 個練習
5.0 Linear Classifiers5分鐘
5.0 Linear Classifiers5分鐘
5.1 Logistic Regression: Prediction10分鐘
Bernoulli Distribution and Maximum Likelihood Estimation5分鐘
5.3 Logistic Regression Cross Entropy Loss10分鐘
完成時間為 2 小時
Softmax Rergresstion
完成時間為 2 小時
3 個視頻 （總計 18 分鐘）
3 個練習
6.1 Softmax Function:Using Lines to Classify Data5分鐘
6.2 Softmax Prediction5分鐘
6.3 Softmax PyTorch Quizz5分鐘
完成時間為 3 小時
Shallow Neural Networks
完成時間為 3 小時
6 個視頻 （總計 33 分鐘）
6 個視頻
More Hidden Neurons2分鐘
Neural Networks with Multiple Dimensional Input5分鐘
7.4 Multi-Class Neural Networks5分鐘
7.5 Backpropagation5分鐘
7.5 Activation Functions4分鐘
6 個練習
Neural Networks5分鐘
More Hidden Neurons 5分鐘
Neural Networks with Multiple Dimensional Inputs5分鐘
Multi-Class Neural Networks5分鐘
Backpropagation5分鐘
Activation Functions5分鐘
審閱
來自DEEP NEURAL NETWORKS WITH PYTORCH的熱門評論
由 SY 提供2020年4月29日
An extremely good course for anyone starting to build deep learning models. I am very satisfied at the end of this course as i was able to code models easily using pytorch. Definitely recomended!!
由 RA 提供2020年5月15日
This is not a bad course at all. One feedback, however, is making the quizzes longer, and adding difficult questions especially concept-based one in the quiz will be more rewarding and valuable.
由 ME 提供2020年3月29日
this course provides a very good and cohesive introduction to Neural Networks. I learned a lot during my journey and I recommend it for anyone interesting in the field.
由 AN 提供2020年3月6日
It was a very informative and interesting lecture. I learn a lot about the details when using PyTorch to build and train a deep neural network. I am so thankful.
關於 IBM AI Engineering 專業證書
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.
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