After this workshop, you will be able to:
- Describe what deep neural networks (DNNs) are, what they can be used for, and how they fit with other AI techniques.
- Explain what computer games have to do with the success of deep learning.
- Describe several deep learning technologies and their tradeoffs.
- Explain the limitations and challenges of using DNNs.
- Train and tune simple DNNs and evaluate their performance, using Python and several deep learning frameworks.
- Describe how DNNs are likely to affect your field in the next 5-10 years.
- Work on case studies in Keras,TensorFlow and Apache Spark
This is a hands-on workshop with many examples and case studies to reinforce learning.
On day one, we will review the core techniques in Deep learning neural networks. Through examples we will understand the different deep learning techniques and frameworks
What you will learn
- Introduction to deep neural networks
- Hands on with Keras and TensorFlow
- Intro to IBM Data Science Experience
- Convolutional neural networks
- Recurrent neural networks for translation, sentiment detection, and other text applications
- Case study 1: Classifying images using fully connected neural networks and convolutional networks
- Case study 2: Monitoring network learning and status using ad-hoc plots and TensorBoard
- Case study 3: Comparing performance of GPU and CPU network training
- Case study 4: Using pre-trained models for identifying objects in photos.
On day two, we will discuss more advanced techniques in deep learning. We will also discuss best practices in scaling and using deep learning techniques including using Apache Spark with Deep learning.