[Packtpub] Deep Learning for Python Developers
Use your Python skills to build powerful Deep Learning applications
Deep learning is a new superpower which will let you build AI systems that just weren’t possible a few years ago. It’s time to utilize intelligent automation to help your business grow, keep organized, and stay on top of the competition.
This course is for Python developers who haven’t worked with machine learning or data science, and want to build intelligent systems right away—without taking a math degree! You will learn about recurrent neural networks, Backprop, SGD, and more. You will work on code examples that are used in a developer’s life on a daily basis; you’ll not only master the theory, you’ll also see how to applied it in the industry as a whole. You will practice all these ideas in MxNet, TensorFlow, Keras, and Gluon. Last but not the least, build Convolutional Neural Networks and apply them to image data.
Deep Learning is currently enabling numerous exciting applications in speech recognition, music synthesis, machine translation, natural language understanding, and many others. AI is transforming multiple industries. After finishing this course, you will likely find creative ways to apply it to your work. We will help you master Deep Learning, understand how to apply it, and build a career in AI.
This video course adopts a practical approach and covers the basics of Deep Learning: perceptrons, activation functions, layering, backpropagation, and more. Each essential concept is immediately followed by hands-on labs which will implement the concept in code and show the practical aspects of Deep Learning theory. In the later sections, when covering deployment and best practices, a mini-project is implemented as a running example and brings together all the material learned before and applies it in a practical manner.
What You Will Learn
- Write software that reads handwriting, classifies images by what’s in them, and decodes messages in sign language—even if you’ve never done machine learning before.
- Build Deep Learning models that work at a much faster rate, never sleep, and get your tasks done quickly and efficiently
- Develop an appreciation of Deep Learning models that are used in large scale, distributed settings in cloud computing
- Apply your sequence models to natural language problems such as including text synthesis and audio applications, speech recognition, and music synthesis.
- Build Convolutional Neural Networks, including recent variations such as residual networks
- Understand the strengths and weaknesses of various frameworks and when it is best to apply them
- Use intelligent automation to avoid errors and save the time normally spent fixing them.
Size: 486.29 MB