The goal of this online 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 專項課程俄罗斯国家研究型高等经济大学課程信息
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第 1 門課程（共 7 門）
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, 意大利語, 越南語, 韓語, 德語（German）, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
您將獲得的技能
- Recurrent Neural Network
- Tensorflow
- Convolutional Neural Network
- Deep Learning
可靈活調整截止日期
根據您的日程表重置截止日期。
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）
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, 意大利語, 越南語, 韓語, 德語（German）, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
提供方
俄罗斯国家研究型高等经济大学
HSE University 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.
授課大綱 - 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
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
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）
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）
審閱
來自INTRODUCTION TO DEEP LEARNING的熱門評論
由 AH 提供2021年1月29日
Great material, but uses obsolete versions of Tensorflow and needs updating generally. Challenging but educational labs. Lecturer accents are not too bad and lectures are fairly good. I learned a lot.
由 EM 提供2020年6月22日
The overall course quality is very decent with real industry applications. I think if you added some additional lecture videos, the course would be better; sometimes it feels like we are in a rush.
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.
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.
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