Machine learning/AI Description
This course covers the development of a real-world application powered by TensorFlow and Keras. TensorFlow is popular software created by Google (and open source contributors) to facilitate the development of machine learning applications, particularly those that use deep learning. Keras is an interface that facilitates the development of deep learning models.
Machine learning/AI Overview
The course starts with a hands-on introduction to TensorFlow and Keras. Then we move to the architecture of an example model, selecting the right layers to solve an example problem (predicting Bitcoin prices). Then we move on to the training and evaluation of the model. We will finish by deploying the model as a real-world product: a web-application (with an HTTP API) that uses Flask to make our model predictions available to the world.
This is a 2-day course packaged with the right balance of theory and hands-on activities that will help you easily learn TensorFlow and Keras from scratch.
Machine learning/AI Scope
This course will provide you with a blueprint of how to build an application that generates predictions using a deep learning model. From there you can continue to improve the example model—either by adding more data, computing more features, or changing its architecture—continuously increasing its prediction accuracy, or create a completely new model, changing the core components of the application as you see fit.
This course will get you started on creating a real-world application by providing the following:
Machine learning/AI Target Audience:
This course is designed for developers, analysts, and data scientists interested in developing applications using TensorFlow and Keras.
Machine learning/AI Course-specific Technical Requirements
- A blueprint of the complete process for deploying a deep learning application: from environment setup to model deployment.
- A hands-on introduction to TensorFlow and Keras, popular technologies for building production-grade deep learning models.
- An example web-application that uses an HTTP API interface to retrieve model predictions.
For successful completion of this course, students will require computer systems with the following:
- Processor: 2.6 GHz or higher, preferably multi-core
- Memory: 4 GB RAM
- Hard disk: 10 GB
- Internet connection
Machine learning/AI Course duration
Machine learning/AI Course outline
- Operating System: Windows (8 or higher).
- Visual Studio Code: https://code.visualstudio.com/ .
- Python 3: Follow instructions in website: https://www.python.org/downloads/
- TensorFlow 1.4 or higher on Windows: Follow instructions in website: https://www.tensorflow.org/install/install_windows .
- Keras 2: Follow instructions in website (Keras only): https://keras.io/#installation .
Lesson 1: Introduction to Neural Networks and Deep Learning
Lesson 2: Model Architecture
- What are Neural Networks?
- Configuring a Deep Learning Environment
Lesson 3: Model Evaluation and Evaluation
- Choosing the Right Model Architecture
- Using Keras as a TensorFlow Interface
Lesson 4: Productization
- Model Evaluation
- Hyperparameter Optimization
- Handling New Data
- Deploying a Model as a Web Application