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Deep learning with applications usin...

Manaswi, Navin Kumar.

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Deep learning with applications using Python = chatbots and face, object, and speech recognition with TensorFlow and Keras /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning with applications using Python/ by Navin Kumar Manaswi.
Reminder of title:	chatbots and face, object, and speech recognition with TensorFlow and Keras /
Author:	Manaswi, Navin Kumar.
Published:	Berkeley, CA :Apress : : 2018.,
Description:	xiii, 219 p. :ill., digital ;24 cm.
[NT 15003449]:	1. Basics of Tensorflow -- 2. Basics of Keras -- 3. Multilayered Perceptron -- 4. Regression to MLP in Tensorflow -- 5. Regression to MLP in Keras -- 6. CNN in Visuals -- 7. CNN with Tensorflow -- 8. CNN with Keras -- 9. RNN and LSTM -- 10. Speech to Text and Vice Versa -- 11. Developing Chatbots -- 12. Face Detection and Face Recognition.
Contained By:	Springer eBooks
Subject:	Python (Computer program language) -
Online resource:	http://dx.doi.org/10.1007/978-1-4842-3516-4
ISBN:	9781484235164

Deep learning with applications using Python = chatbots and face, object, and speech recognition with TensorFlow and Keras /
Manaswi, Navin Kumar.

Deep learning with applications using Pythonchatbots and face, object, and speech recognition with TensorFlow and Keras /[electronic resource] :by Navin Kumar Manaswi. - Berkeley, CA :Apress :2018. - xiii, 219 p. :ill., digital ;24 cm.

1. Basics of Tensorflow -- 2. Basics of Keras -- 3. Multilayered Perceptron -- 4. Regression to MLP in Tensorflow -- 5. Regression to MLP in Keras -- 6. CNN in Visuals -- 7. CNN with Tensorflow -- 8. CNN with Keras -- 9. RNN and LSTM -- 10. Speech to Text and Vice Versa -- 11. Developing Chatbots -- 12. Face Detection and Face Recognition.

Build deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and Keras. This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications. Deep Learning with Applications Using Python covers topics such as chatbots, natural language processing, and face and object recognition. The goal is to equip you with the concepts, techniques, and algorithm implementations needed to create programs capable of performing deep learning. This book covers intermediate and advanced levels of deep learning, including convolutional neural networks, recurrent neural networks, and multilayer perceptrons. It also discusses popular APIs such as IBM Watson, Microsoft Azure, and scikit-learn. You will: Work with various deep learning frameworks such as TensorFlow, Keras, and scikit-learn. Build face recognition and face detection capabilities Create speech-to-text and text-to-speech functionality Make chatbots using deep learning.

ISBN: 9781484235164

Standard No.: 10.1007/978-1-4842-3516-4doiSubjects--Topical Terms:

729789
Python (Computer program language)

LC Class. No.: QA76.73.P98

Dewey Class. No.: 005.133

Deep learning with applications using Python = chatbots and face, object, and speech recognition with TensorFlow and Keras /
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520 $a Build deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and Keras. This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications. Deep Learning with Applications Using Python covers topics such as chatbots, natural language processing, and face and object recognition. The goal is to equip you with the concepts, techniques, and algorithm implementations needed to create programs capable of performing deep learning. This book covers intermediate and advanced levels of deep learning, including convolutional neural networks, recurrent neural networks, and multilayer perceptrons. It also discusses popular APIs such as IBM Watson, Microsoft Azure, and scikit-learn. You will: Work with various deep learning frameworks such as TensorFlow, Keras, and scikit-learn. Build face recognition and face detection capabilities Create speech-to-text and text-to-speech functionality Make chatbots using deep learning.
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