Deep Learning with Keras

Deep Learning with Keras

Keras is a high-level neural networks API for fast development and experimentation. It runs on top of TensorFlow or CNTK.

This instructor-led, live training (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

Format of the Course

Interactive lectures and discussions
Lots exercises and practice
Questions&Answers sessions for clarifications and problem-solving
Live project implementation and demonstrations

Here is what you will get with this course:

Introduction
Overview of Neural Networks
Understanding Convolutional Networks
Setting up Keras
Overview of Keras Features and Architecture
Overview of Keras Syntax
Understanding How a Keras Model Organize Layers
Configuring the Keras Backend (TensorFlow or Theano)
Implementing an Unsupervised Learning Model
Analyzing Images with a Convolutional Neural Network (CNN)
Preprocessing Data
Training the Model
Training on CPU vs GPU vs TPU
Evaluating the Model
Using a Pre-trained Deep Learning Model
Setting up a Recurrent Neural Network (RNN)
Debugging the Model
Saving the Model
Deploying the Model
Monitoring a Keras Model with TensorBoard
Troubleshooting
Summary and Conclusion

Who this course is for:

Python Programming experience
Experience with the Linux command line



	Dates Recommended Trainings

	Duration 3 Days Price per participant 2.450 EUR

	Language/Documentation English Germany

	Participants Developers Data scientists

Contact

If you are interested in a company-specific custom development and would like to find out more, please feel free to get in touch with us.

Give us a call on: +49 (0) 176 310 693 62

or send an email to: info@inovaitec.com

Alternatively, You can fill out our contact form here. We look forward to hearing from you.