About this Course
4.5
550 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....
Globe

100% online courses

Start instantly and learn at your own schedule.
Calendar

Flexible deadlines

Reset deadlines in accordance to your schedule.
Advanced Level

Advanced Level

Clock

Approx. 36 hours to complete

Suggested: 6 weeks of study, 6-10 hours/week...
Comment Dots

English

Subtitles: English...

Skills you will gain

Recurrent Neural NetworkTensorflowConvolutional Neural NetworkDeep Learning
Globe

100% online courses

Start instantly and learn at your own schedule.
Calendar

Flexible deadlines

Reset deadlines in accordance to your schedule.
Advanced Level

Advanced Level

Clock

Approx. 36 hours to complete

Suggested: 6 weeks of study, 6-10 hours/week...
Comment Dots

English

Subtitles: English...

Syllabus - What you will learn from this course

Week
1
Clock
5 hours to complete

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....
Reading
9 videos (Total 63 min), 2 readings, 3 quizzes
Video9 videos
Course intro6m
Linear regression9m
Linear classification10m
Gradient descent5m
Overfitting problem and model validation6m
Model regularization5m
Stochastic gradient descent5m
Gradient descent extensions9m
Reading2 readings
Welcome!5m
Hardware for the course10m
Quiz2 practice exercises
Linear models6m
Overfitting and regularization8m
Week
2
Clock
6 hours to complete

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....
Reading
9 videos (Total 85 min), 3 readings, 4 quizzes
Video9 videos
Chain rule7m
Backpropagation9m
Efficient MLP implementation13m
Other matrix derivatives5m
What is TensorFlow10m
Our first model in TensorFlow10m
What Deep Learning is and is not8m
Deep learning as a language6m
Reading3 readings
Optional reading on matrix derivatives1m
TensorFlow reading1m
Keras reading1m
Quiz2 practice exercises
Multilayer perceptron10m
Matrix derivatives20m
Week
3
Clock
5 hours to complete

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....
Reading
6 videos (Total 59 min), 3 quizzes
Video6 videos
Our first CNN architecture10m
Training tips and tricks for deep CNNs14m
Overview of modern CNN architectures8m
Learning new tasks with pre-trained CNNs5m
A glimpse of other Computer Vision tasks8m
Quiz1 practice exercise
Convolutions and pooling10m
Week
4
Clock
4 hours to complete

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....
Reading
9 videos (Total 81 min), 3 quizzes
Video9 videos
Autoencoders 1015m
Autoencoder applications9m
Autoencoder applications: image generation, data visualization & more7m
Natural language processing primer10m
Word embeddings13m
Generative models 1017m
Generative Adversarial Networks10m
Applications of adversarial approach11m
Quiz1 practice exercise
Word embeddings8m
4.5
Direction Signs

22%

started a new career after completing these courses
Briefcase

83%

got a tangible career benefit from this course

Top Reviews

By YGJan 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!

By ASMar 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.

Instructors

Evgeny Sokolov

Senior Lecturer
HSE Faculty of Computer Science

Andrei Zimovnov

Senior Lecturer
HSE Faculty of Computer Science

Alexander Panin

Lecturer
HSE Faculty of Computer Science

Ekaterina Lobacheva

Senior Lecturer
HSE Faculty of Computer Science

Nikita Kazeev

Researcher
HSE Faculty of Computer Science

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

About the Advanced Machine Learning Specialization

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....
Advanced Machine Learning

Frequently Asked Questions

  • 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.

  • 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.

More questions? Visit the Learner Help Center.