Deep Learning Essentials
Welcome to Deep Learning Essentials!
Deep learning is a machine learning method that takes in an input X, and uses it to predict an output of Y. The “deep” part of deep learning refers to creating deep neural networks, neural network contains virtual ‘neurons’ that are arranged in layers that are connected to each other. The neurons pass on the information and thereby perform calculations. This course is designed to teach you about neurons and other deep learning core concepts.
At the end of this course, you will learn the theory behind deep learning, about neural networks and important deep learning algorithms such as ANN, CNN and RNN. You will also learn about most used deep learning models. Interesting thing? You will code your first neural network using Python and scikit-learn.
My goal with this course is to give you a start of most complex field of artificial intelligence, so that you can apply these algorithms in real project, on your job and add it to your profile to get experience you need for your career goal.
What’s In This Course:
- A thorough introduction to deep learning
- Deep learning core concepts
- Three types of learning
- Artificial neural networks (ANN)
- Convolutional neural network (CNN)
- Recurrent neural network (RNN)
- Future research
This is an advance level course, so before starting this course you must have knowledge of these topics:
- Prior knowledge of machine learning and machine learning algorithms
- Intermediate knowledge of python programming
- Knowledge of multi-variant calculus and linear algebra.
Who This Course Is For:
- Data scientist, software engineers.
- AI practitioners.
- Anyone who is interested in learning about machine learning and deep learning.