Understanding and coding complex deep studying algorithms with the main intuitive deep studying library in existence
- A whole and updated creation to GANs
- A entire review of Keras
- A dive into complex deep studying sticking to the basic mathematics
Keras allows a brand new new release of deep studying builders to entry the total strength of TensorFlow at the one hand, whereas focusing on construction purposes at the different. much more remarkable is the facility to jot down functions drawing from the facility of recent algorithms, with out truly having to enforce all of the algorithms, considering the fact 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 is taking the strategy of creating on really recognized ways and algorithms, sooner than introducing newer advancements to operating builders. He then asks the reader to write down and comprehend an NLP software to turn out the sensible price of autoencoders written with Keras.
The middle of the booklet lies within the insurance of a number of periods of antagonistic networks (GANs). Dr. Atienza specializes in the latest successes of GANs and teaches builders to enforce more recent effects for themselves, caution them of pitfalls and exhibiting them the benefits of each one. He focuses specifically on picture iteration and synthesis.
Finally, the booklet finishes with an advent to reinforcement studying, utilizing OpenAI fitness center as a framework to simplify experimenting with a variety of regulations and algorithms. back, Keras is the unifying layer wherein OpenAI fitness center is accessed.
Overall, complex Keras indicates the entire services of Keras to a developer, whereas attempting to steer clear of taking a look at underlying infrastructure supplied through TensorFlow, Theano or Microsoft Cognitive providers. Dr. Atienza is displaying the best way to get new algorithms to paintings inside of Keras, with no getting the reader tangled in too many implementation details.
What you'll learn
- You will examine Keras thoroughly
- To code photograph synthesis examples with GANs
- To distinguish a variety of different types of adverse networks and enforce them
- To write a reinforcement studying software with OpenAI gym
- To step clear of vintage deep studying and laptop studying and write production-ready functions in keeping with fresh research
Who This booklet Is For
Familiarity with Python and uncomplicated computing device studying is critical for this publication and it might be most excellent if the reader had understood a number of easy 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's usually considered as the prime college of the rustic. He has a PhD from Australian nationwide collage. His learn parts comprise robotics, imaginative and prescient, photographs, language processing and figuring out, and VR/AR, and he has written approximately snap shots, 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