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 162A
  • 02-342 Warsaw
  • Poland
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