Keras is an open source Python neural-network library for the creation of deep learning neural-networks. Keras offers an intuitive set of abstractions, simplifying the development of deep learning neural-networks and models.
This instructor-led, live training (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.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
- An understanding of basic linear algebra
- Software Engineers
Keras and Deep Learning Frameworks
- TensorFlow and Theano back-ends
- Keras vs Tensorflow
Data and Machine Learning
- Tabular data, visual data, unstructured data, etc.
- Unsupervised learning, supervised learning, reinforcement learning, etc.
Preparing the Development Environment
- Installing and configuring Anaconda
- Installing Keras with a TensorFlow back-end
Neural Networks in Keras
- Using Keras functional API to build a network
- Pre-processing and fitting data
- Defining a Keras model
Mutiple Input and Output Networks
- Building two input-networks
- Representing high-cardinality data
- Merging layers
- Extending the two input-network
- Building neural networks with multiple outputs
- Solving multiple problems simultaneously
Training and Pre-Training
- Training models
- Saving and loading models
- Using ResNet50 on models
- Exporting Keras logs
- Visualizing a computational graph and training progress
- Exporting models
- Uploading Keras models
- Using a model in Google Cloud
Summary and Conclusion