Product description: Machine Learning - Neural Network Design
The course Machine Learning - Neural Network Design addresses topics for developers and enthusiasts of new technologies such as machine learning (ML, Machine Learning) and deep learning (DL, Deep Learning). It covers artificial intelligence algorithms, creating them and optimizing them. An intermediate level course, it is part of the Machine Learning Engineer course path.
Overview of course content
- Neural network learning algorithms
- Regression, classification and anomaly detection in neural networks
- Optuna library and optimization of learning algorithms
- Neural network design in TensorFlow library
- Recursive and convolutional neural networks
- Function optimization methods
- Current trends in Machine Learning
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Machine Learning that's coming
Machine learning is playing an increasingly important role in our lives. Solutions based on artificial intelligence automate many repetitive processes to ease our workload. The solutions of this technology are used not only by market giants like Google or Facebook. Neural networks are one of the most groundbreaking algorithms in this field, which helps to solve increasingly complex problems. There is a constantly growing demand for engineers specializing in ML and DL, who lay the foundation for new methods of work in many areas of human activity.
Where are neural networks hiding?
Automatic dyeing of black and white films, sound de-noising or complex solutions for medical diagnostics are systems based on neural networks. The number of fields in which similar solutions are used is still growing. During the course you will learn how to use them in your projects. There is no deep dive here - lectures will first remind you of the basics and introduce you to the history of neural networks in a nutshell.
Structure and optimization in AI work
The topics of neural networks and machine learning optimization are closely related. Correlated examples are discussed based on the popular Optuna library. For example, if you are planning to launch a new service, optimization methods can simplify the process of selecting the best pricing strategy for you. Many of the examples in the course simultaneously teach you how to maximize reward and reduce costs.
The course discusses autoencoders (auto-encoders, autocoders), recurrent networks, and convolutional networks. These are the leading methods in terms of popularity and effectiveness when working with AI. They have helped solve many problems that until now proved too difficult for traditional algorithmics and early Machine Learning methods. These solutions are used in research on issues such as mutation analysis for genetic diseases, retinopathy or oncology diagnosis. The course shows how to use them in your own projects and applications.
- Unlimited access, including 24/7 mobile access
- 7 hours of training
- Tests and assignments
- 35 professional lectures
- 26 test questions
- Certificate of completion
Artificial intelligence course supplement
Neural networks and machine learning in the broadest sense are part of the area known as artificial intelligence. We recommend that you also become familiar with the items:
Table of contents1 Introduction