With this course, you'll learn how to train, evaluate, and deploy Tensorflow and Keras models as real-world web applications. After a hands-on introduction to neural networks and deep learning, you'll use a sample model to explore details of deep learning and learn to select the right layers that can solve a given problem. By the end of the course, you'll build a Bitcoin application that predicts the future price, based on historic and freely available information.
- Learn ways to select the right model architecture
- Make predictions with a trained model and work with TensorBoard
- Evaluate metrics and techniques to deploy a model as a web application
- Set up a deep learning programming environment
- Explore components of a neural network and its essential operations
- Deploy model as an interactive web application with Flask and HTTP API
- Learn to use Keras, a TensorFlow abstraction library
- Explore types of problems that are addressed by neural networks
For an optimal student experience, we recommend the following hardware configuration:
- Processor: 2.6 GHz or higher, preferably multi-core
- Memory: 4GB RAM
- Hard disk: 10GB or more
- An Internet connection
You’ll also need the following software installed in advance:
- Operating System: Windows (8 or higher)
- Visual Studio Code
- Python 3
- TensorFlow 1.4 or higher on Windows: Follow instructions in website
- Keras 2