With the advent of better compute and big data, enterprises are looking at leveraging the latest and the best techniques in artificial intelligence, or to be more specific deep learning. The effectiveness of deep neural networks has been widespread in solving diverse problems across the industry, thanks to open-source tools like tensorflow & keras.
We'll cover deep learning basics, deep neural network architectures, popular open-source tools and talk about some real-world case studies in the industry which are being solved using deep learning. This is not intended to be a theoretical lecture about neural networks but more of a practical session talking about applied deep learning techniques and case-studies in the industry.
Session Outline: - Introduction
- Deep Learning Basics
- Deep Learning Effectiveness
- Deep Learning Frameworks
- Deep Learning Model Architectures
- Case Studies
Case Studies:
- Predicting Data Center Device Failures
- Pro-active Incident Resolution
- Malaria Detection (includes hands-on code walkthrough)
- Pro-active Security Vulnerability Detection for golang-Kubernetes-OpenShift eco-system (includes hands-on code walkthrough)
We will showcase the hands-on code walkthrough using Jupyter notebooks which you can re-use from my GitHub in the future. Code will be in tensorflow \ keras.
