The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Learners will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers.
提供方
高级机器学习 專項課程国立高等经济大学課程信息
180,582 次近期查看
學生職業成果
29%
完成這些課程後已開始新的職業生涯
35%
通過此課程獲得實實在在的工作福利
19%
加薪或升職
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 1 門課程（共 7 門）
可靈活調整截止日期
根據您的日程表重置截止日期。
高級
完成時間大約為34 小時
英語（English）
字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 中文（簡體）, 意大利語, 越南語, 韓語, 德語（German）, 俄語（Russian）, 土耳其語（Turkish）, 英語（English）, 西班牙語（Spanish）
您將獲得的技能
Recurrent Neural NetworkTensorflowConvolutional Neural NetworkDeep Learning
學生職業成果
29%
完成這些課程後已開始新的職業生涯
35%
通過此課程獲得實實在在的工作福利
19%
加薪或升職
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 1 門課程（共 7 門）
可靈活調整截止日期
根據您的日程表重置截止日期。
高級
完成時間大約為34 小時
英語（English）
字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 中文（簡體）, 意大利語, 越南語, 韓語, 德語（German）, 俄語（Russian）, 土耳其語（Turkish）, 英語（English）, 西班牙語（Spanish）
提供方
国立高等经济大学
National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communicamathematics, engineering, and more.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 6 小時
Introduction to optimization
Welcome to the "Introduction to Deep Learning" course! In the first week you'll learn about linear models and stochatic optimization methods. Linear models are basic building blocks for many deep architectures, and stochastic optimization is used to learn every model that we'll discuss in our course.
完成時間為 6 小時
10 個視頻 （總計 64 分鐘）, 4 個閱讀材料, 3 個測驗
10 個視頻
Welcome to AML specialization!2分鐘
Course intro6分鐘
Linear regression9分鐘
Linear classification10分鐘
Gradient descent5分鐘
Overfitting problem and model validation6分鐘
Model regularization5分鐘
Stochastic gradient descent5分鐘
Gradient descent extensions9分鐘
4 個閱讀材料
About the University10分鐘
Rules on the academic integrity in the course10分鐘
Welcome!5分鐘
Hardware for the course10分鐘
2 個練習
Linear models30分鐘
Overfitting and regularization30分鐘
完成時間為 5 小時
Introduction to neural networks
This module is an introduction to the concept of a deep neural network. You'll begin with the linear model and finish with writing your very first deep network.
完成時間為 5 小時
9 個視頻 （總計 85 分鐘）, 3 個閱讀材料, 4 個測驗
9 個視頻
Chain rule7分鐘
Backpropagation9分鐘
Efficient MLP implementation13分鐘
Other matrix derivatives5分鐘
What is TensorFlow10分鐘
Our first model in TensorFlow10分鐘
What Deep Learning is and is not8分鐘
Deep learning as a language6分鐘
3 個閱讀材料
Optional reading on matrix derivatives1分鐘
TensorFlow reading1分鐘
Keras reading1分鐘
2 個練習
Multilayer perceptron10分鐘
Matrix derivatives20分鐘
完成時間為 6 小時
Deep Learning for images
In this week you will learn about building blocks of deep learning for image input. You will learn how to build Convolutional Neural Network (CNN) architectures with these blocks and how to quickly solve a new task using so-called pre-trained models.
完成時間為 6 小時
6 個視頻 （總計 59 分鐘）
6 個視頻
Our first CNN architecture10分鐘
Training tips and tricks for deep CNNs14分鐘
Overview of modern CNN architectures8分鐘
Learning new tasks with pre-trained CNNs5分鐘
A glimpse of other Computer Vision tasks8分鐘
1 個練習
Convolutions and pooling30分鐘
完成時間為 5 小時
Unsupervised representation learning
This week we're gonna dive into unsupervised parts of deep learning. You'll learn how to generate, morph and search images with deep learning.
完成時間為 5 小時
9 個視頻 （總計 81 分鐘）
9 個視頻
Autoencoders 1015分鐘
Autoencoder applications9分鐘
Autoencoder applications: image generation, data visualization & more7分鐘
Natural language processing primer10分鐘
Word embeddings13分鐘
Generative models 1017分鐘
Generative Adversarial Networks10分鐘
Applications of adversarial approach11分鐘
1 個練習
Word embeddings30分鐘
審閱
來自INTRODUCTION TO DEEP LEARNING的熱門評論
由 DK 提供2019年9月19日
one of the excellent courses in deep learning. As stated its advanced and enjoyed a lot in solving the assignments. looking forward for more such courses especially in Natural language processing
由 TP 提供2020年8月8日
A very good course and it is truly insightful. This course deals with more on the concepts therefore I have a better understanding of what is really happening when I build deep learning models.
由 AM 提供2020年5月28日
The hardest, yet most satisfying course I've ever taken in deep learning, by the end of the course I was doing stuff that was borderline sci-fi and that was just "introduction" to deep learning
由 MO 提供2017年12月16日
I found the content to be interesting and on a good level of advancement, but I also found the exercises to be buggy sometimes or not well thought, which cost a lot of extra time spent on it.
關於 高级机器学习 專項課程
This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings.
常見問題
我什么时候能够访问课程视频和作业？
我订阅此专项课程后会得到什么？
Is financial aid available?
完成课程后，我会获得大学学分吗？
還有其他問題嗎？請訪問 學生幫助中心。
