Introduction
Participants will firstly get themselves prepared by learning the basic linear algebra and essential programming skills for the implementation of deep learning algorithms. Participants will experience the exciting development process of applying various AI models/platforms/tools and API to build their own AI applications. In the carefully planned lab exercises, participants can follow the step-by-step instructions to build and train the AI models. The most exciting part is to learn from more than 20 hands-on lab practices like Image Classification, Lyric Generation, Time-series Prediction, Video Object Detection and so on.
Target Audience:
Management, Business Analyst, Financial Analyst, IT practitioner
Prerequisites:
To get the most out of the course, participants are expected to have some basic understanding in linear algebra and programming concepts
Course Contents
1. Get Prepared
l Linear algebra, Python essentials, underlying principles of deep learning
2. Deep Learning Fundamentals in AI
l How deep learning works?
3. Popular AI Frameworks
l ANN (Scikit Learn), CNN / RNN / LSTM (TensorFlow), Keras Framework
4. Implementing Deep Learning Algorithms
l Build neural network and train model
5. Implementing Business Applications in AI
l Stock prediction, customer churn rate analysis, recommendation system for buyers, face recognition, etc.
Mobile AI applications and Tensorflow-Lite (offline access)
