本课程是 Advanced Machine Learning 专项课程 专项课程的一部分

提供方

Advanced Machine Learning 专项课程

National Research University Higher School of Economics

课程信息

4.5

545 ratings

•

137 reviews

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.
Learners will use these building blocks to define complex modern architectures in TensorFlow and Keras frameworks. In the course project learner will implement deep neural network for the task of image captioning which solves the problem of giving a text description for an input image.
The prerequisites for this course are:
1) Basic knowledge of Python.
2) Basic linear algebra and probability.
Please note that this is an advanced course and we assume basic knowledge of machine learning. You should understand:
1) Linear regression: mean squared error, analytical solution.
2) Logistic regression: model, cross-entropy loss, class probability estimation.
3) Gradient descent for linear models. Derivatives of MSE and cross-entropy loss functions.
4) The problem of overfitting.
5) Regularization for linear models....

立即开始，按照自己的计划学习。

根据您的日程表重置截止日期。

完成时间大约为36 小时

字幕：English

Recurrent Neural NetworkTensorflowConvolutional Neural NetworkDeep Learning

立即开始，按照自己的计划学习。

根据您的日程表重置截止日期。

完成时间大约为36 小时

字幕：English

章节

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....

9 个视频（共 63 分钟）, 2 个阅读材料, 3 个测验

Course intro6分钟

Linear regression9分钟

Linear classification10分钟

Gradient descent5分钟

Overfitting problem and model validation6分钟

Model regularization5分钟

Stochastic gradient descent5分钟

Gradient descent extensions9分钟

Welcome!5分钟

Hardware for the course10分钟

Linear models6分钟

Overfitting and regularization8分钟

章节

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....

9 个视频（共 85 分钟）, 3 个阅读材料, 4 个测验

Chain rule7分钟

Backpropagation9分钟

Efficient MLP implementation13分钟

Other matrix derivatives5分钟

What is TensorFlow10分钟

Our first model in TensorFlow10分钟

What Deep Learning is and is not8分钟

Deep learning as a language6分钟

Optional reading on matrix derivatives1分钟

TensorFlow reading1分钟

Keras reading1分钟

Multilayer perceptron10分钟

Matrix derivatives20分钟

章节

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 个视频（共 59 分钟）, 3 个测验

Our first CNN architecture10分钟

Training tips and tricks for deep CNNs14分钟

Overview of modern CNN architectures8分钟

Learning new tasks with pre-trained CNNs5分钟

A glimpse of other Computer Vision tasks8分钟

Convolutions and pooling10分钟

章节

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....

9 个视频（共 81 分钟）, 3 个测验

Autoencoders 1015分钟

Autoencoder applications9分钟

Autoencoder applications: image generation, data visualization & more7分钟

Natural language processing primer10分钟

Word embeddings13分钟

Generative models 1017分钟

Generative Adversarial Networks10分钟

Applications of adversarial approach11分钟

Word embeddings8分钟

4.5

完成这些课程后已开始新的职业生涯

通过此课程获得实实在在的工作福利

创建者 YG•Jan 28th 2018

This is a very hands on Deep Learning class. Like the design of programming assignments a lot. It's very instructive as well as challenging! Great course. I would recommend it!

创建者 AS•Mar 26th 2018

Great course! The faculty does an excellent job in explaining some difficult to understand concepts. The discussion forum is very active and the course community is helpful.

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 communications, IT, mathematics, engineering, and more.
Learn more on www.hse.ru...

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....

When will I have access to the lectures and assignments?

Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

What is the refund policy?

Is financial aid available?

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