Intelligent projects using Python :: 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras /
This book includes 9 projects on building smart and practical AI-based systems. These projects cover solutions to different domain-specific problems in healthcare, e-commerce and more. With this book, you will apply different machine learning and deep learning techniques and learn how to build your...
Gespeichert in:
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt,
[2019]
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This book includes 9 projects on building smart and practical AI-based systems. These projects cover solutions to different domain-specific problems in healthcare, e-commerce and more. With this book, you will apply different machine learning and deep learning techniques and learn how to build your own intelligent applications for smart ... |
| Beschreibung: | CNNs and LSTMs in video captioning |
| Beschreibung: | 1 online resource (332 pages) |
| ISBN: | 9781788994866 1788994868 |
Internformat
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| 505 | 0 | |a Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Foundations of Artificial Intelligence Based Systems; Neural networks; Neural activation units; Linear activation units; Sigmoid activation units; The hyperbolic tangent activation function; Rectified linear unit (ReLU); The softmax activation unit; The backpropagation method of training neural networks; Convolutional neural networks; Recurrent neural networks (RNNs); Long short-term memory (LSTM) cells; Generative adversarial networks; Reinforcement learning; Q-learning | |
| 505 | 8 | |a Deep Q-learning Transfer learning; Restricted Boltzmann machines; Autoencoders ; Summary; Chapter 2: Transfer Learning; Technical requirements; Introduction to transfer learning; Transfer learning and detecting diabetic retinopathy; The diabetic retinopathy dataset ; Formulating the loss function; Taking class imbalances into account; Preprocessing the images ; Additional data generation using affine transformation; Rotation ; Translation; Scaling ; Reflection; Additional image generation through affine transformation; Network architecture ; The VGG16 transfer learning network | |
| 505 | 8 | |a The InceptionV3 transfer learning networkThe ResNet50 transfer learning network; The optimizer and initial learning rate; Cross-validation; Model checkpoints based on validation log loss ; Python implementation of the training process; Dynamic mini batch creation during training ; Results from the categorical classification; Inference at testing time ; Performing regression instead of categorical classification ; Using the keras sequential utils as generator ; Summary; Chapter 3: Neural Machine Translation; Technical requirements; Rule-based machine translation; The analysis phase | |
| 505 | 8 | |a Lexical transfer phase Generation phase ; Statistical machine-learning systems; Language model ; Perplexity for language models; Translation model; Neural machine translation; The encoder-decoder model; Inference using the encoder-decoder model; Implementing a sequence-to-sequence neural translation machine; Processing the input data; Defining a model for neural machine translation; Loss function for the neural translation machine; Training the model; Building the inference model; Word vector embeddings; Embeddings layer; Implementing the embeddings-based NMT; Summary | |
| 505 | 8 | |a Chapter 4: Style Transfer in Fashion Industry using GANsTechnical requirements; DiscoGAN; CycleGAN; Learning to generate natural handbags from sketched outlines; Preprocess the Images; The generators of the DiscoGAN; The discriminators of the DiscoGAN; Building the network and defining the cost functions; Building the training process; Important parameter values for GAN training; Invoking the training; Monitoring the generator and the discriminator loss ; Sample images generated by DiscoGAN; Summary; Chapter 5: Video Captioning Application; Technical requirements | |
| 500 | |a CNNs and LSTMs in video captioning | ||
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| contents | Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Foundations of Artificial Intelligence Based Systems; Neural networks; Neural activation units; Linear activation units; Sigmoid activation units; The hyperbolic tangent activation function; Rectified linear unit (ReLU); The softmax activation unit; The backpropagation method of training neural networks; Convolutional neural networks; Recurrent neural networks (RNNs); Long short-term memory (LSTM) cells; Generative adversarial networks; Reinforcement learning; Q-learning Deep Q-learning Transfer learning; Restricted Boltzmann machines; Autoencoders ; Summary; Chapter 2: Transfer Learning; Technical requirements; Introduction to transfer learning; Transfer learning and detecting diabetic retinopathy; The diabetic retinopathy dataset ; Formulating the loss function; Taking class imbalances into account; Preprocessing the images ; Additional data generation using affine transformation; Rotation ; Translation; Scaling ; Reflection; Additional image generation through affine transformation; Network architecture ; The VGG16 transfer learning network The InceptionV3 transfer learning networkThe ResNet50 transfer learning network; The optimizer and initial learning rate; Cross-validation; Model checkpoints based on validation log loss ; Python implementation of the training process; Dynamic mini batch creation during training ; Results from the categorical classification; Inference at testing time ; Performing regression instead of categorical classification ; Using the keras sequential utils as generator ; Summary; Chapter 3: Neural Machine Translation; Technical requirements; Rule-based machine translation; The analysis phase Lexical transfer phase Generation phase ; Statistical machine-learning systems; Language model ; Perplexity for language models; Translation model; Neural machine translation; The encoder-decoder model; Inference using the encoder-decoder model; Implementing a sequence-to-sequence neural translation machine; Processing the input data; Defining a model for neural machine translation; Loss function for the neural translation machine; Training the model; Building the inference model; Word vector embeddings; Embeddings layer; Implementing the embeddings-based NMT; Summary Chapter 4: Style Transfer in Fashion Industry using GANsTechnical requirements; DiscoGAN; CycleGAN; Learning to generate natural handbags from sketched outlines; Preprocess the Images; The generators of the DiscoGAN; The discriminators of the DiscoGAN; Building the network and defining the cost functions; Building the training process; Important parameter values for GAN training; Invoking the training; Monitoring the generator and the discriminator loss ; Sample images generated by DiscoGAN; Summary; Chapter 5: Video Captioning Application; Technical requirements |
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| spelling | Pattanayak, Santanu, author. Intelligent projects using Python : 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras / Santanu Pattanayak. Birmingham : Packt, [2019] ©2019 1 online resource (332 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Foundations of Artificial Intelligence Based Systems; Neural networks; Neural activation units; Linear activation units; Sigmoid activation units; The hyperbolic tangent activation function; Rectified linear unit (ReLU); The softmax activation unit; The backpropagation method of training neural networks; Convolutional neural networks; Recurrent neural networks (RNNs); Long short-term memory (LSTM) cells; Generative adversarial networks; Reinforcement learning; Q-learning Deep Q-learning Transfer learning; Restricted Boltzmann machines; Autoencoders ; Summary; Chapter 2: Transfer Learning; Technical requirements; Introduction to transfer learning; Transfer learning and detecting diabetic retinopathy; The diabetic retinopathy dataset ; Formulating the loss function; Taking class imbalances into account; Preprocessing the images ; Additional data generation using affine transformation; Rotation ; Translation; Scaling ; Reflection; Additional image generation through affine transformation; Network architecture ; The VGG16 transfer learning network The InceptionV3 transfer learning networkThe ResNet50 transfer learning network; The optimizer and initial learning rate; Cross-validation; Model checkpoints based on validation log loss ; Python implementation of the training process; Dynamic mini batch creation during training ; Results from the categorical classification; Inference at testing time ; Performing regression instead of categorical classification ; Using the keras sequential utils as generator ; Summary; Chapter 3: Neural Machine Translation; Technical requirements; Rule-based machine translation; The analysis phase Lexical transfer phase Generation phase ; Statistical machine-learning systems; Language model ; Perplexity for language models; Translation model; Neural machine translation; The encoder-decoder model; Inference using the encoder-decoder model; Implementing a sequence-to-sequence neural translation machine; Processing the input data; Defining a model for neural machine translation; Loss function for the neural translation machine; Training the model; Building the inference model; Word vector embeddings; Embeddings layer; Implementing the embeddings-based NMT; Summary Chapter 4: Style Transfer in Fashion Industry using GANsTechnical requirements; DiscoGAN; CycleGAN; Learning to generate natural handbags from sketched outlines; Preprocess the Images; The generators of the DiscoGAN; The discriminators of the DiscoGAN; Building the network and defining the cost functions; Building the training process; Important parameter values for GAN training; Invoking the training; Monitoring the generator and the discriminator loss ; Sample images generated by DiscoGAN; Summary; Chapter 5: Video Captioning Application; Technical requirements CNNs and LSTMs in video captioning This book includes 9 projects on building smart and practical AI-based systems. These projects cover solutions to different domain-specific problems in healthcare, e-commerce and more. With this book, you will apply different machine learning and deep learning techniques and learn how to build your own intelligent applications for smart ... Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Intelligence artificielle. Python (Langage de programmation) artificial intelligence. aat Artificial intelligence fast Python (Computer program language) fast has work: Intelligent projects using Python (Text) https://id.oclc.org/worldcat/entity/E39PCGYxd4pD8hrh3F9c9XKyBP https://id.oclc.org/worldcat/ontology/hasWork Print version: Pattanayak, Santanu. Intelligent Projects Using Python : 9 Real-World AI Projects Leveraging Machine Learning and Deep Learning with TensorFlow and Keras. Birmingham : Packt Publishing Ltd, ©2019 9781788996921 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2018969 Volltext |
| spellingShingle | Pattanayak, Santanu Intelligent projects using Python : 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras / Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Foundations of Artificial Intelligence Based Systems; Neural networks; Neural activation units; Linear activation units; Sigmoid activation units; The hyperbolic tangent activation function; Rectified linear unit (ReLU); The softmax activation unit; The backpropagation method of training neural networks; Convolutional neural networks; Recurrent neural networks (RNNs); Long short-term memory (LSTM) cells; Generative adversarial networks; Reinforcement learning; Q-learning Deep Q-learning Transfer learning; Restricted Boltzmann machines; Autoencoders ; Summary; Chapter 2: Transfer Learning; Technical requirements; Introduction to transfer learning; Transfer learning and detecting diabetic retinopathy; The diabetic retinopathy dataset ; Formulating the loss function; Taking class imbalances into account; Preprocessing the images ; Additional data generation using affine transformation; Rotation ; Translation; Scaling ; Reflection; Additional image generation through affine transformation; Network architecture ; The VGG16 transfer learning network The InceptionV3 transfer learning networkThe ResNet50 transfer learning network; The optimizer and initial learning rate; Cross-validation; Model checkpoints based on validation log loss ; Python implementation of the training process; Dynamic mini batch creation during training ; Results from the categorical classification; Inference at testing time ; Performing regression instead of categorical classification ; Using the keras sequential utils as generator ; Summary; Chapter 3: Neural Machine Translation; Technical requirements; Rule-based machine translation; The analysis phase Lexical transfer phase Generation phase ; Statistical machine-learning systems; Language model ; Perplexity for language models; Translation model; Neural machine translation; The encoder-decoder model; Inference using the encoder-decoder model; Implementing a sequence-to-sequence neural translation machine; Processing the input data; Defining a model for neural machine translation; Loss function for the neural translation machine; Training the model; Building the inference model; Word vector embeddings; Embeddings layer; Implementing the embeddings-based NMT; Summary Chapter 4: Style Transfer in Fashion Industry using GANsTechnical requirements; DiscoGAN; CycleGAN; Learning to generate natural handbags from sketched outlines; Preprocess the Images; The generators of the DiscoGAN; The discriminators of the DiscoGAN; Building the network and defining the cost functions; Building the training process; Important parameter values for GAN training; Invoking the training; Monitoring the generator and the discriminator loss ; Sample images generated by DiscoGAN; Summary; Chapter 5: Video Captioning Application; Technical requirements Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Intelligence artificielle. Python (Langage de programmation) artificial intelligence. aat Artificial intelligence fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85008180 http://id.loc.gov/authorities/subjects/sh96008834 |
| title | Intelligent projects using Python : 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras / |
| title_auth | Intelligent projects using Python : 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras / |
| title_exact_search | Intelligent projects using Python : 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras / |
| title_full | Intelligent projects using Python : 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras / Santanu Pattanayak. |
| title_fullStr | Intelligent projects using Python : 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras / Santanu Pattanayak. |
| title_full_unstemmed | Intelligent projects using Python : 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras / Santanu Pattanayak. |
| title_short | Intelligent projects using Python : |
| title_sort | intelligent projects using python 9 real world ai projects leveraging machine learning and deep learning with tensorflow and keras |
| title_sub | 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras / |
| topic | Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Intelligence artificielle. Python (Langage de programmation) artificial intelligence. aat Artificial intelligence fast Python (Computer program language) fast |
| topic_facet | Artificial intelligence. Python (Computer program language) Intelligence artificielle. Python (Langage de programmation) artificial intelligence. Artificial intelligence |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2018969 |
| work_keys_str_mv | AT pattanayaksantanu intelligentprojectsusingpython9realworldaiprojectsleveragingmachinelearninganddeeplearningwithtensorflowandkeras |