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by The Data Guy (Author)
Learn, Revise, and Recap several important deep learning concepts, key terms, and algorithms ranging from a feedforward neural network to generative adversarial networks and many more.
Why should you get this book?
- A perfectly designed pocket reference guide to learn and revise deep learning at anytime, anywhere.
- This guide comes in handy for a quick reference during work and also for preparing deep learning interviews.
- The book includes cool animated characters and keeps you engaged.
Book description
Deep learning which is a subset of machine learning has changed the landscape of artificial intelligence and is prominently used for many interesting applications like natural language processing (NLP), computer vision (CV), predictive analytics, and many more. This book, Deep Learning Pocket Reference Guide walks you through a collection of the most important deep learning algorithms, key concepts, terms, and terminologies.
This book can be used for quickly learning, revising, and recapping deep learning concepts. This book can also be used as a quick reference guide to prepare for deep learning interviews. You will learn and revise deep learning algorithms like artificial neural networks, recurrent neural networks, convolutional networks, generative adversarial networks, autoencoders, and many others. You will also look into concepts like weight initialization, batch normalization, drop out, learning rate scheduling, and many more.
In the ever-changing world of data science and artificial intelligence, this quick reference guide to deep learning will help you to revise the deep learning concepts with cool animated characters. With this deep learning pocket reference guide, you can revise the algorithms anytime and anywhere you are.
Is it the right book for you?
If you already know deep learning and want to quickly revise and recap deep learning algorithms then this is the perfect book for you. When you are preparing for a deep learning interview or stuck at work, this book comes in handy for quickly going through all the deep learning concepts.
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