The virtual course "Introduction to deep learning - Virtual Course - Coursera", is a course with different contents and that offers video classes of Approx. 34 hours to complete. Explore its essential features, and click the orange button to get detailed information on the Coursera e-Learning platform
The goal of this course is to provide students with a basic understanding of modern neural networks and their applications in computer vision and natural language understanding.
The course begins with an overview of linear models and a discussion of stochastic optimization methods that are crucial for training deep neural networks.
Students will study all of the popular building blocks of neural networks, including fully connected, convolutional, and recurrent layers.
Students will use these building blocks to define complex modern architectures in the TensorFlow and Keras frameworks.
In the course project, the student will implement a deep neural network for the image caption task that 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: root mean square error, analytical solution.
2) Logistic regression: model, cross-entropy loss, class probability estimation.
3) Gradient descent for linear models.
MSE derivatives and cross entropy loss functions.
4) The problem of overfitting.
5) Regularization for linear models.
Do you have technical problems? Write us: 5) Regularization for linear models.
Do you have technical problems? Write us: 5) Regularization for linear models.
Do you have technical problems? Write to us: Coursera@hse.
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