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.
Do you have technical problems? Write to us: firstname.lastname@example.org
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.
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.
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.
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.
Deep learning for sequences
In this week you will learn how to use deep learning for sequences such as texts, video, audio, etc. You will learn about several Recurrent Neural Network (RNN) architectures and how to apply them for different tasks with sequential input/output.
In this week you will apply all your knowledge about neural networks for images and texts for the final project. You will solve the task of generating descriptions for real world images!
Nikita Kazeev, Andrei Zimovnov, Alexander Panin, Evgeny Sokolov and Ekaterina Lobacheva
This is a very nice course, it's part of the advanced machine learning specialization so it would make sense if the lecturers go fast through some mathematical intricacies. lecturers are not obviously native speakers but who cares, they speak clearly...
This is a very nice course, it's part of the advanced machine learning specialization so it would make sense if the lecturers go fast through some mathematical intricacies. lecturers are not obviously native speakers but who cares, they speak clearly and that's all that matters to me (subtitles were invented for a reason...)
if you are used to Andrew Ng's way of introducing concepts you might dislike this one (at least at first). Andrew's philosophy is to "democratize" AI education so he tends to simplify concepts(especially maths) for better digestion.
These guys do not do that(and there is nothing wrong with that...) because, i think, they address a different audience
After all the specialization's name is ADVANCED machine learning, keep the momentum HSE, you're doing great !
Anonymous is taking this course right now.
Teachers speak fast and not understandable. Concepts are not explained well either. Practicals need better explanation and should be strongly related to what is thought in the course