Detailed Course Outline
Module 1: Deep Learning Fundamentals
- The role of Training datasets in AI
- Introduction to Neural Networks and Deep Learning
- Essential Deep Learning Theory
- Deep Learning Frameworks and Libraries
Module 2: Deep Learning with Keras, TensorFlow’s High-Level API
- Shallow Neural Network in Keras
- Learning with Artificial Neurons
- TensorFlow Playground – visualizing a Deep Net in Action
Module 3: Introduction to TensorFlow
- TensorFlow Primer (matrix operations, computation graphs and data types)
- Build and Train Deep Nets on TensorFlow
- Handling data in TensorFlow (fitting, hyper-parameter tuning)
Module 4: Deep Learning with TensorFlow
- Elements of TensorFlow Graph
- Constructing and Executing a TensorFlow Graph
- Training a (Simple) Linear model in TensorFlow
Module 5: Working with TensorFlow Graphs
- Elements of TensorFlow Graph
- Constructing and Executing a TensorFlow Graph
- Training a (Simple) Linear model in TensorFlow
Module 6: Dense Neural Networks in TensorFlow
- Review a Dense Net in Keras
- Designing a Dense Net in TensorFlow
- Training a Dense Net in TensorFlow
Module 7: Convolutional Neural Networks in TensorFlow
- Review a ConvNet in Keras
- ConvNets in TensorFlow
- When to use TensorFlow vs Keras or another API
Module 8a: Project: Deep Learning Application for Machine Computer Vision
Module 8b: Project: Deep Learning Application for Natural Language Processing (NLP)
Module 9: Summary and Project Review