Understanding and coding complex deep studying algorithms with the main intuitive deep studying library in existence
- A entire and updated creation to GANs
- A entire evaluate of Keras
- A dive into complicated deep studying sticking to the fundamental mathematics
Keras permits a brand new new release of deep studying builders to entry the total strength of TensorFlow at the one hand, whereas targeting development purposes at the different. much more amazing is the facility to write down purposes drawing from the facility of latest algorithms, with out truly having to enforce the entire algorithms, considering that they're already available.
After introducing Keras and familiarizing the reader with Keras through classical deep studying algorithms, Dr. Atienza walks the developer via autoencoders first. he's taking the method of creating on quite famous methods and algorithms, ahead of introducing newer advancements to operating builders. He then asks the reader to write down and comprehend an NLP program to turn out the sensible worth of autoencoders written with Keras.
The center of the e-book lies within the assurance of a number of periods of antagonistic networks (GANs). Dr. Atienza makes a speciality of the latest successes of GANs and teaches builders to enforce more recent effects for themselves, caution them of pitfalls and displaying them the benefits of every one. He focuses specifically on snapshot iteration and synthesis.
Finally, the e-book finishes with an advent to reinforcement studying, utilizing OpenAI gymnasium as a framework to simplify experimenting with numerous rules and algorithms. back, Keras is the unifying layer in which OpenAI gymnasium is accessed.
Overall, complicated Keras indicates the total functions of Keras to a developer, whereas attempting to stay away from taking a look at underlying infrastructure supplied by means of TensorFlow, Theano or Microsoft Cognitive providers. Dr. Atienza is exhibiting easy methods to get new algorithms to paintings inside Keras, with out getting the reader tangled in too many implementation details.
What you'll learn
- You will study Keras thoroughly
- To code snapshot synthesis examples with GANs
- To distinguish a number of kinds of opposed networks and enforce them
- To write a reinforcement studying software with OpenAI gym
- To step clear of vintage deep studying and desktop studying and write production-ready functions in response to contemporary research
Who This booklet Is For
Familiarity with Python and simple computer studying is important for this publication and it'd be most suitable if the reader had understood numerous simple deep studying algorithms, like CNNs and RNNs.
About the Author
Professor Rowel Atienza is an affiliate professor on the collage of the Philippines, Diliman, that is typically considered as the prime college of the rustic. He has a PhD from Australian nationwide collage. His learn parts contain robotics, imaginative and prescient, pics, language processing and realizing, and VR/AR, and he has written approximately pix, digital fact, and the human-robot interface. he's additionally well-published in deep learning.
This is his first booklet for the broader developer community.
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Extra resources for Advanced Deep Learning with Keras: Applying GANs and other new deep learning algorithms to the real world
Advanced Deep Learning with Keras: Applying GANs and other new deep learning algorithms to the real world