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：
上級機械学習 の専門講座ロシア国立研究大学経済高等学院（National Research University Higher School of Economics）このコースについて
513,534 最近の表示
受講生の就業成果
35%
コース終了後に新しいキャリアをスタートした
40%
コースが具体的なキャリアアップにつながった
21%
昇給や昇進につながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における7の1コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
上級レベル
約34時間で修了
英語
字幕：英語, 韓国語
習得するスキル
Recurrent Neural NetworkTensorflowConvolutional Neural NetworkDeep Learning
受講生の就業成果
35%
コース終了後に新しいキャリアをスタートした
40%
コースが具体的なキャリアアップにつながった
21%
昇給や昇進につながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における7の1コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
上級レベル
約34時間で修了
英語
字幕：英語, 韓国語
提供：
ロシア国立研究大学経済高等学院（National Research University Higher School of Economics）
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.
シラバス - 本コースの学習内容
6時間で修了
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.
6時間で修了
10件のビデオ （合計64分）, 3 readings, 3 quizzes
10件のビデオ
Welcome to AML specialization!2 分
Course intro6 分
Linear regression9 分
Linear classification10 分
Gradient descent5 分
Overfitting problem and model validation6 分
Model regularization5 分
Stochastic gradient descent5 分
Gradient descent extensions9 分
3件の学習用教材
About the University10 分
Welcome!5 分
Hardware for the course10 分
2の練習問題
Linear models30 分
Overfitting and regularization30 分
5時間で修了
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.
5時間で修了
9件のビデオ （合計85分）, 3 readings, 4 quizzes
9件のビデオ
Chain rule7 分
Backpropagation9 分
Efficient MLP implementation13 分
Other matrix derivatives5 分
What is TensorFlow10 分
Our first model in TensorFlow10 分
What Deep Learning is and is not8 分
Deep learning as a language6 分
3件の学習用教材
Optional reading on matrix derivatives1 分
TensorFlow reading1 分
Keras reading1 分
2の練習問題
Multilayer perceptron10 分
Matrix derivatives20 分
6時間で修了
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.
6時間で修了
6件のビデオ （合計59分）
6件のビデオ
Our first CNN architecture10 分
Training tips and tricks for deep CNNs14 分
Overview of modern CNN architectures8 分
Learning new tasks with pre-trained CNNs5 分
A glimpse of other Computer Vision tasks8 分
1の練習問題
Convolutions and pooling30 分
5時間で修了
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.
5時間で修了
9件のビデオ （合計81分）
9件のビデオ
Autoencoders 1015 分
Autoencoder applications9 分
Autoencoder applications: image generation, data visualization & more7 分
Natural language processing primer10 分
Word embeddings13 分
Generative models 1017 分
Generative Adversarial Networks10 分
Applications of adversarial approach11 分
1の練習問題
Word embeddings30 分
レビュー
INTRODUCTION TO DEEP LEARNING からの人気レビュー
DKにより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
AMにより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
MOにより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.
AKにより2019年6月1日
one of the best courses I have attended. clear explanation, clear examples, amazing quizzes & Programming Assignment this course is advanced level, don't enroll it if you are a new starter.
上級機械学習専門講座について
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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When will I have access to the lectures and assignments?
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The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
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What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
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