Course
Introduction to Deep Learning in Python
IntermediateSkill Level
Updated 11/2022Start Course for Free
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PythonArtificial Intelligence4 hr17 videos50 Exercises3,500 XP260K+Statement of Accomplishment
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Discover Deep Learning Applications
Deep learning is the machine learning technique behind the most exciting capabilities in robotics, natural language processing, image recognition, and artificial intelligence. In this 4-hour course, you’ll gain hands-on practical knowledge of how to apply your Python skills to deep learning with the Keras 2.0 library.Explore Keras Models with a Library Contributor
Taught by ex-Google data scientist and Keras contributor, Dan Becker, this deep learning course explores neural network models and how you can generate predictions with them. The first chapters will grow your understanding of both forward and backward propagation and how they work in practice.Keras library is a Python library that can help you develop and review deep learning models. Like many Python libraries, it's free, open-source and very user friendly. You’ll start by creating a Keras model and will learn how to compile, fit, and classify it before making predictions. Once you’ve completed this course, you’ll have all the tools you need to build deep neural networks and start experimenting with wider and deeper networks over time.
Delve Further into Deep Learning
This course is part of several machine learning and deep learning tracks, offering you clear pathways to build your skills and experience in this area once you’ve completed the introductory course, whether you want to complete a personal project or move towards a career as a Machine Learning Scientist.Prerequisites
Supervised Learning with scikit-learn1
Basics of deep learning and neural networks
In this chapter, you'll become familiar with the fundamental concepts and terminology used in deep learning, and understand why deep learning techniques are so powerful today. You'll build simple neural networks and generate predictions with them.
2
Optimizing a neural network with backward propagation
Learn how to optimize the predictions generated by your neural networks. You'll use a method called backward propagation, which is one of the most important techniques in deep learning. Understanding how it works will give you a strong foundation to build on in the second half of the course.
3
Building deep learning models with keras
In this chapter, you'll use the Keras library to build deep learning models for both regression and classification. You'll learn about the Specify-Compile-Fit workflow that you can use to make predictions, and by the end of the chapter, you'll have all the tools necessary to build deep neural networks.
4
Fine-tuning keras models
Learn how to optimize your deep learning models in Keras. Start by learning how to validate your models, then understand the concept of model capacity, and finally, experiment with wider and deeper networks.
Introduction to Deep Learning in Python
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Enroll NowFAQs
What is deep learning?
Deep learning is a type of machine learning and AI that aims to imitate how humans build certain types of knowledge by using neural networks instead of simple algorithms.
Is deep learning possible with Python?
Yes. There are a large number of popular deep learning libraries available in the Python ecosystem, including Keras and PyTorch. Python is often considered an essential skill for anybody who would like to build a career in machine learning.
What is Keras?
Keras is a deep learning API developed by Google for implementing neural networks. It’s free, open source, and is written in Python. Within machine learning, it’s used to help with the implementation of neural networks.
Is Python good for building neural networks?
Yes. Python is a popular coding language because of its flexibility and concise, readable code, which is also why it’s widely used in neural networks. Python’s simplicity allows developers to write reliable systems while focusing on the machine learning problem itself rather than the language.
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