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Advanced Deep Learning Techniques

The course is intended for people who are looking for a deeper understanding of artificial neural networks, especially so called deep learning.
Level
Designed for participants with advanced knowledge and experience
advanced
Course length
1 day
Language
 cz  eu
Course code
KT21110289
Artificial intelligence (AI)
Category:
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Courses with lecturer

Term
Language
Place
Form
?
How and where the course takes place.
Price without VAT
Open term
?
We will agree on a specific date together. This is a non-binding order.
Language
Place
Praha
Form
classroom
?
The course with an instructor in classroom.
Code of the course: KT21110289-0003
Price without VAT
4 990 Kč
Open term
?
We will agree on a specific date together. This is a non-binding order.
Language
Place
Praha
Form
classroom
?
The course with an instructor in classroom.
Code of the course: KT21110289-0004
Price without VAT
4 990 Kč

Course description

We will build on the basic knowledge of machine learning principles on the level of our course Introduction to machine learning. We will pay special attention to the topic of machine learning model interpretability and explainability.

Required knowledge

  • basic knowledge of programing in Python
  • high school level of mathematics
  • Basics of machine learning on the level of our course Introduction to machine Learning

Course content

  • Neural network architectures (feed-forward, recurrent, convolutional, generative, autoencoders, Unet, GAN, attention layer)
  • Optimizers and their evolution (Steepest Gradient Descent, Stochastic Gradient Descent, Mini-Batch Gradient Descent, Nesterov Accelerated Gradient, Adagrad, AdaDelta, Adam, Learning rate tuning)
  • Loss functions and their properties (Mean squared error, Mean absolute error, Negative, Log Likelihood – cross entropy)
  • Regularization in Neural Networks (Dropout, Early stopping, Data augmentation, Batch and layer normalization)
  • Initialization (Gradient vanishing problem, Zero initialization, He initialization, Xavier initialization)
  • Semi-supervised learning (Pseudo Labeling, Mean-Teacher, PI-Model)
  • Practical examples of semi-supervised techniques applications
  • Confidence estimation (Logit analysis, Confidence networks)
  • Practical examples of confidence estimation
  • AutoML approaches (Hyper-parameter optimization, grid search, Bayesian optimization, Meta-Learning, Neural network search)
  • Practical examples with the AutoKeras
  • ML Explainability (Interpretable models, Partial Dependence Plot, Permutation feature importance, Surrogate models, Activation Maximization, Grad CAM)

Lecturers

Jiří Materna
Jiří Materna

He is a machine learning specialist with experience in its applications in industry since 2007. Between 2008 and 2017, he worked at Seznam.cz, of which the last 7 years as head of the research department. He now works as a freelancer, offers the development of custom machine learning solutions, organizes the Machine Learning Prague conference and writes the ML Guru blog. 

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Previous courses

Do you want this tailor-made course for your company?

Contact us

News with the course

Náhledový obrázek novinky
Artificial intelligence (AI) 15. 11. 2024
The Practical Application of AI: HubSpot Increased Lead Conversion Rate

HubSpot is an American software development company. It has 8,000 employees and branches in several countries around the world. Processes related to lead generation and qualification were time-consuming and not always efficient, reducing sales productivity.

Náhledový obrázek novinky
Artificial intelligence (AI) 17. 10. 2024

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DHL is one of the largest logistics companies in the world. It provides transportation and delivery of parcels in  many countries. However, route planning, warehouse management and demand forecasting were difficult and often inefficient.

Náhledový obrázek novinky
Artificial intelligence (AI) 12. 9. 2024
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Microsoft, a global tech giant, faced challenges with its complex financial processes, which involved extensive financial planning and analysis (FP&A). Traditional methods were time-consuming and sometimes inaccurate, leading to delayed decisions and potential financial risks.

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