Introduction to Deep Learning
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: email@example.com
- Fecha Incio:27/01/2020
- Idioma: Inglés
- Universidad: Higher School of Economics
- Profesores: Nikita Kazeev, Andrei Zimovnov, Alexander Panin, Evgeny Sokolov and Ekaterina Lobacheva
- Certificado: No