Introduction to deep learning

Machine learning training

Prerequisites

Python syntax
Solid knowledge of basic machine learning ideas
(validation methods, gradient descent algorithm, preventing overfitting)
machine learning experience

Skills your team will gain

Knowledge of selected deep learning concepts and algorithms.

The ability to create deep learning algorithms in PyTorch or Keras.

The ability to create pipelines for solving real-life problems.

Tools and metrics for evaluating deep learning models.

Duration

2 days

Agenda

Part 1

Deep learning algorithms with PyTorch or Keras

  • The programming environment: PyTorch or Keras
  • Layer types in detail: dense, convolutional, max-pooling layers
  • Case study: fully connected network, convolutional neural network

Part 2

Different architectures for different needs

  • Transfer learning
  • Highlight of advanced deep learning architectures
  • U-Net implementation

Contact us

The administrator of the personal data provided by you in the registration form is deepsense.ai sp. z o.o., headquartered at al. Jerozolimskie 44, 00-024 Warsaw, Poland. Your personal data will be processed for the purpose of directing marketing content to you.
Detailed information about the processing of your personal data, including your rights, can be found in our privacy policy.
* This consent is required to receive email communication from deepsense.ai sp. z o.o. regarding the company and its offerings.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Locations
  • deepsense.ai, Inc.
  • 2100 Geng Road, Suite 210
  • Palo Alto, CA 94303
  • United States of America
  • deepsense.ai Sp. z o.o.
  • al. Jerozolimskie 44
  • 00-024 Warsaw
  • Poland
  • ul. Łęczycka 59
  • 85-737 Bydgoszcz
  • Poland
Let us know how we can help