37 Keras courses delivered On Demand

This course is a quick starter for anyone looking to delve into optical character recognition, image recognition, object detection, and object recognition using Python without having to deal with all the complexities and mathematics associated with a typical deep learning process.

This course is designed around three main activities for getting better results with deep learning models: better or faster learning, better generalization to new data, and better predictions when using final models. Take this course if you're passionate about deep learning with a solid foundation in this space and want to learn how to squeeze the best performance out of your deep learning models.

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Are you fascinated with Netflix and YouTube recommendations and how they accurately recommend content that you would like to watch? Are you looking for a practical course that will teach you how to build intelligent recommendation systems? This course will show you how to build accurate recommendation systems in Python using real-world examples.

This comprehensive deep learning course with Python will start with the basics and work up to advanced topics such as using different frameworks in Python to solve real-world problems and building artificial neural networks with TensorFlow and Keras.

In this self-paced course, you will learn how to use TensorFlow 2 to build deep neural networks. You will learn the basics of machine learning, classification, and regression. We will also discuss the connection between artificial and biological neural networks and how that inspires our thinking in deep learning.

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This comprehensive course will help you learn the basics to advanced mechanisms of chatbot development using deep learning with Python. This course is a complete package for beginners to learn chatbot fundamentals with deep learning and its applications and build it from scratch using deep learning (RNNs) with Python.

In this course, you will learn how to author machine learning models in Python without the aid of frameworks or libraries from scratch. Discover the process of loading data, evaluating models, and implementing machine learning algorithms.

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