Keras Training Courses

Keras Training Courses

Lokale, door een instructeur geleide live Keras trainingscursussen demonstreren door interactieve praktische oefening hoe Keras te gebruiken voor snelle experimenten met diepe neurale netwerken. Keras training is beschikbaar als "live training op locatie" of "live training op afstand". Live training op locatie kan lokaal worden uitgevoerd bij de klant in Nederland of in NobleProg bedrijfsopleidingscentra in Nederland . Live-training op afstand wordt uitgevoerd via een interactieve, externe desktop. NobleProg - Uw lokale trainingsaanbieder

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Keras Cursusoverzicht

Course Name
Duration
Overview
Course Name
Duration
Overview
21 hours
Overview
This instructor-led, live training in Nederland (online or onsite) is aimed at technical persons who wish to apply deep learning model to image recognition applications.

By the end of this training, participants will be able to:

- Install and configure Keras.
- Quickly prototype deep learning models.
- Implement a convolutional network.
- Implement a recurrent network.
- Execute a deep learning model on both a CPU and GPU.
21 hours
Overview
This instructor-led, live training in Nederland (online or onsite) is aimed at developers who wish to build a self-driving car using deep learning techniques.

By the end of this training, participants will be able to:

- Use Keras to build and train a convolutional neural network.
- Use computer vision techniques to identify lanes in an autonomos driving project.
- Train a deep learning model to differentiate traffic signs.
- Simulate a fully autonomous car.
14 hours
Overview
This instructor-led, live training in Nederland (online or onsite) is aimed at software engineers who wish to develop advanced deep learning neural-networks and model using Keras and Python.

By the end of this training, participants will be able to:

- Apply deep learning with supervised or unsupervised learning methods.
- Develop, train, and implement concurrent neural networks and recurrent neural networks.
- Use Keras and Python to build deep learning models to solve problems involving images, text, sound, and more.

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