عنوان

Deep learning with applications using Python :

شابک

1484235169

شابک

1484235177

شابک

1484240510

شابک

9781484235164

شابک

9781484235171

شابک

9781484240519

شابک اشتباه

1484235150

شابک اشتباه

9781484235157

عنوان اصلي

Deep learning with applications using Python :

نام عام مواد

[Book]

ساير اطلاعات عنواني

chatbots and face, object, and speech recognition with TensorFlow and Keras /

نام نخستين پديدآور

Navin Kumar Manaswi.

محل نشرو پخش و غیره

[Berkeley, CA] :

نام ناشر، پخش کننده و غيره

Apress,

تاریخ نشرو بخش و غیره

2018.

نام خاص و کميت اثر

1 online resource

متن يادداشت

Intro; Table of Contents; Foreword; About the Author; About the Technical Reviewer; Chapter 1: Basics of TensorFlow; Tensors; Computational Graph and Session; Constants, Placeholders, and Variables; Placeholders; Creating Tensors; Fixed Tensors; Sequence Tensors; Random Tensors; Working on Matrices; Activation Functions; Tangent Hyperbolic and Sigmoid; ReLU and ELU; ReLU6; Loss Functions; Loss Function Examples; Common Loss Functions; Optimizers; Loss Function Examples; Common Optimizers; Metrics; Metrics Examples; Common Metrics; Chapter 2: Understanding and Working with Keras

متن يادداشت

Chapter 5: Regression to MLP in KerasLog-Linear Model; Keras Neural Network for Linear Regression; Logistic Regression; scikit-learn for Logistic Regression; Keras Neural Network for Logistic Regression; Fashion MNIST Data: Logistic Regression in Keras; MLPs on the Iris Data; Write the Code; Build a Sequential Keras Model; MLPs on MNIST Data (Digit Classification); MLPs on Randomly Generated Data; Chapter 6: Convolutional Neural Networks; Different Layers in a CNN; CNN Architectures; Chapter 7: CNN in TensorFlow; Why TensorFlow for CNN Models?

متن يادداشت

Major Steps to Deep Learning ModelsLoad Data; Preprocess the Data; Define the Model; Compile the Model; Fit the Model; Evaluate Model; Prediction; Save and Reload the Model; Optional: Summarize the Model; Additional Steps to Improve Keras Models; Keras with TensorFlow; Chapter 3: Multilayer Perceptron; Artificial Neural Network; Single-Layer Perceptron; Multilayer Perceptron; Logistic Regression Model; Chapter 4: Regression to MLP in TensorFlow; TensorFlow Steps to Build Models; Linear Regression in TensorFlow; Logistic Regression Model; Multilayer Perceptron in TensorFlow

متن يادداشت

TensorFlow Code for Building an Image Classifier for MNIST DataUsing a High-Level API for Building CNN Models; Chapter 8: CNN in Keras; Building an Image Classifier for MNIST Data in Keras; Define the Network Structure; Define the Model Architecture; Building an Image Classifier with CIFAR-10 Data; Define the Network Structure; Define the Model Architecture; Pretrained Models; Chapter 9: RNN and LSTM; The Concept of RNNs; The Concept of LSTM; Modes of LSTM; Sequence Prediction; Sequence Numeric Prediction; Sequence Classification; Sequence Generation; Sequence-to-Sequence Prediction

متن يادداشت

Time-Series Forecasting with the LSTM ModelChapter 10: Speech to Text and Vice Versa; Speech-to-Text Conversion; Speech as Data; Speech Features: Mapping Speech to a Matrix; Spectrograms: Mapping Speech to an Image; Building a Classifier for Speech Recognition Through MFCC Features; Building a Classifier for Speech Recognition Through a Spectrogram; Open Source Approaches; Examples Using Each API; Using PocketSphinx; Using the Google Speech API; Using the Google Cloud Speech API; Using the Wit.ai API; Using the Houndify API; Using the IBM Speech to Text API

بدون عنوان

0

بدون عنوان

8

بدون عنوان

8

بدون عنوان

8

بدون عنوان

8

متن يادداشت

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.

منبع سفارش / آدرس اشتراک

Springer Nature

شماره انبار

com.springer.onix.9781484235164

عنوان

Deep learning with applications using Python.

شماره استاندارد بين المللي کتاب و موسيقي

9781484235157

موضوع مستند نشده

Machine learning.

موضوع مستند نشده

Python (Computer program language)

موضوع مستند نشده

Artificial intelligence.

موضوع مستند نشده

COMPUTERS-- Programming Languages-- Python.

موضوع مستند نشده

Databases.

موضوع مستند نشده

Machine learning.

موضوع مستند نشده

Programming & scripting languages: general.

موضوع مستند نشده

Python (Computer program language)

موضوع مستند نشده

COM-- 051360

موضوع مستند نشده

UMA

موضوع مستند نشده

UYQ

شماره

ويراست

شماره رده

مستند نام اشخاص تاييد نشده

Manaswi, Navin Kumar.

تاريخ عمليات

20200823032044.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y