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 our training manager
Find us
- 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
Let us know how we can help
- Our offer
- contact@deepsense.ai
- Media relations
- media@deepsense.ai
