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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第 4 門課程（共 6 門）
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中級
完成時間大約為20 小時
建議：19 hours/week
英語（English）
字幕：英語（English）
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）, 2 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
2 個閱讀材料
Labs10分鐘
Labs Review & Download10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 3 小時
Linear Regression
完成時間為 3 小時
7 個視頻 （總計 35 分鐘）, 1 個閱讀材料, 10 個測驗
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
1 個閱讀材料
Labs Review & Download10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）, 1 個閱讀材料, 8 個測驗
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
1 個閱讀材料
Labs Review & Download10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）, 1 個閱讀材料, 6 個測驗
4 個視頻
Multiple Linear Regression Training2分鐘
Linear Regression Multiple Outputs5分鐘
Multiple Output Linear Regression Training1分鐘
1 個閱讀材料
Labs Review & Download10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）, 1 個閱讀材料, 8 個測驗
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
1 個閱讀材料
5.4 Labs Review & Download10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）, 1 個閱讀材料, 5 個測驗
1 個閱讀材料
6.4 Labs Review & Download10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）, 1 個閱讀材料, 12 個測驗
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
1 個閱讀材料
7.7 Labs Review & Download10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
4.4
來自DEEP NEURAL NETWORKS WITH PYTORCH的熱門評論
由 AN 提供Mar 6, 2020
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.
由 SK 提供Nov 16, 2019
Awesome! This course gives me the basic workflow for using machine learning technique in my research! The materials in the form of Jupyter lab really help!
由 TG 提供Jan 10, 2020
Very intensive course. Could do more training labs. But this is definitely a very dense course. Extremely helpful to get started on ML/Deep Learning.
由 JJ 提供Feb 19, 2020
It was a LONG course, very packed with info. But, I feel like I certainly learned a lot and have a great foundation for further learning.
由 EE 提供Jan 19, 2020
Good, thorough course. Does not hold the student to any kind of standard or accountability and quizzes are ridiculously easy to pass.
由 AZ 提供Mar 24, 2020
It is freeaaakin hard if you take the whole IBM AI ENGINEERING Professional Cert in the duration of a trial period.
由 PD 提供Jan 2, 2020
The course is interesting and well organized but the quiz are not challenging and full of typos.
由 PJ 提供Feb 12, 2020
More graded coding assignments would have been better, but content is good!
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.
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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