Long Short-Term Memory Networks With Python: Develop Sequence Prediction Models With Deep Learning
Jason Brownlee
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
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
Κατηγορίες:
Έτος:
2017
Έκδοση:
v1.0
Εκδότης:
Machine Learning Mastery
Γλώσσα:
english
Σελίδες:
246
Αρχείο:
PDF, 6.78 MB
IPFS:
,
english, 2017
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