Brownlee Jason. Long Short-Term Memory Networks With Python. Develop Sequence Prediction Models With Deep Learning
Welcome to Long Short-Term Memory Networks With Python. Long Short-Term Memory (LSTM) recurrent neural networks are one of the most interesting types of deep learning at the moment. They have been used to demonstrate world-class results in complex problem domains such as language translation, automatic image captioning, and text generation. LSTMs are very different to other deep learning techniques, such as Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs), in that they are designed specifically for sequence prediction problems. I designed this book for you to rapidly discover what LSTMs are, how they work, and how you can bring this important technology to your own sequence prediction problems.
This book will teach you how to get results as a machine learning practitioner interested in using LSTMs on your project. After reading and working through this book, you will know:
What LSTMs are.
Why LSTMs are important.
How LSTMs work.
How to develop a suite of LSTM architectures.
How to get the most out of your LSTM models.
This book will NOT teach you how to be a research scientist and all the theory behind why LSTMs work.
Foundations
What are LSTMs.
How to Train LSTMs.
How to Prepare Data for LSTMs.
How to Develop LSTMs in Keras.
Models for Sequence Prediction.
Models
How to Develop Vanilla LSTMs.
How to Develop Stacked LSTMs.
How to Develop CNN LSTMs.
How to Develop Encoder-Decoder LSTMs.
How to Develop Bidirectional LSTMs.
How to Develop Generative LSTMs.
How to Diagnose and Tune LSTMs.
How to Make Predictions with LSTMs.
How to Update LSTM Models.
Архив Анны
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“Preface
This book was born out of one thought:
If I had to get a machine learning practitioner proficient with LSTMs in two weeks (e.g. capable
of applying LSTMs to their own sequence prediction projects), what would I teach?
I had been researching and applying LSTMs for some time and wanted to write something on
the topic, but struggled for months on how exactly to present it. The above question crystallized
it for me and this whole book came together.
The above motivating question for this book is clarifying. It means that the lessons that I
teach are focused only on the topics that you need to know in order to understand (1) what
LSTMs are, (2) why we need LSTMs and (3) how to develop LSTM”
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