TensorFlow Deep Learning Projects :: 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning.
This book is your guide to master deep learning with TensorFlow, with the help of 10 real-world projects. You will train high-performance models in TensorFlow to generate captions for images automatically, predict stocks' performance, create intelligent chatbots, perform large-scale text classi...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | , , , |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing,
2018.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This book is your guide to master deep learning with TensorFlow, with the help of 10 real-world projects. You will train high-performance models in TensorFlow to generate captions for images automatically, predict stocks' performance, create intelligent chatbots, perform large-scale text classification, develop recommendation systems, and more. |
| Beschreibung: | Chapter 10: Video Games by Reinforcement Learning. |
| Beschreibung: | 1 online resource (310 pages) |
| ISBN: | 9781788398381 1788398386 1788398068 9781788398060 |
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| 100 | 1 | |a Grigorev, Alexey. | |
| 245 | 1 | 0 | |a TensorFlow Deep Learning Projects : |b 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning. |
| 260 | |a Birmingham : |b Packt Publishing, |c 2018. | ||
| 300 | |a 1 online resource (310 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 505 | 0 | |a Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Recognizing traffic signs using Convnets; The dataset; The CNN network; Image preprocessing; Train the model and make predictions; Follow-up questions; Summary; Chapter 2: Annotating Images with Object Detection API; The Microsoft common objects in context; The TensorFlow object detection API; Grasping the basics of R-CNN, R-FCN and SSD models; Presenting our project plan; Setting up an environment suitable for the project; Protobuf compilation; Windows installation; Unix installation. | |
| 505 | 8 | |a Provisioning of the project codeSome simple applications; Real-time webcam detection; Acknowledgements; Summary; Chapter 3: Caption Generation for Images; What is caption generation?; Exploring image captioning datasets; Downloading the dataset; Converting words into embeddings; Image captioning approaches; Conditional random field; Recurrent neural network on convolution neural network; Caption ranking; Dense captioning; RNN captioning; Multimodal captioning; Attention-based captioning; Implementing a caption generation model; Summary; Chapter 4: Building GANs for Conditional Image Creation. | |
| 505 | 8 | |a Introducing GANsThe key is in the adversarial approach; A cambrian explosion; DCGANs; Conditional GANs; The project; Dataset class; CGAN class; Putting CGAN to work on some examples; MNIST; Zalando MNIST; EMNIST; Reusing the trained CGANs; Resorting to Amazon Web Service; Acknowledgements; Summary; Chapter 5: Stock Price Prediction with LSTM; Input datasets -- cosine and stock price; Format the dataset; Using regression to predict the future prices of a stock; Long short-term memory -- LSTM 101; Stock price prediction with LSTM; Possible follow -- up questions; Summary. | |
| 505 | 8 | |a Chapter 6: Create and Train Machine Translation SystemsA walkthrough of the architecture; Preprocessing of the corpora; Training the machine translator; Test and translate; Home assignments; Summary; Chapter 7: Train and Set up a Chatbot, Able to Discuss Like a Human; Introduction to the project; The input corpus; Creating the training dataset; Training the chatbot; Chatbox API; Home assignments; Summary; Chapter 8: Detecting Duplicate Quora Questions; Presenting the dataset; Starting with basic feature engineering; Creating fuzzy features; Resorting to TF-IDF and SVD features. | |
| 505 | 8 | |a Mapping with Word2vec embeddingsTesting machine learning models; Building a TensorFlow model; Processing before deep neural networks; Deep neural networks building blocks; Designing the learning architecture; Summary; Chapter 9: Building a TensorFlow Recommender System; Recommender systems; Matrix factorization for recommender systems; Dataset preparation and baseline; Matrix factorization; Implicit feedback datasets; SGD-based matrix factorization; Bayesian personalized ranking; RNN for recommender systems; Data preparation and baseline; RNN recommender system in TensorFlow; Summary. | |
| 500 | |a Chapter 10: Video Games by Reinforcement Learning. | ||
| 520 | |a This book is your guide to master deep learning with TensorFlow, with the help of 10 real-world projects. You will train high-performance models in TensorFlow to generate captions for images automatically, predict stocks' performance, create intelligent chatbots, perform large-scale text classification, develop recommendation systems, and more. | ||
| 650 | 0 | |a Artificial intelligence. |0 http://id.loc.gov/authorities/subjects/sh85008180 | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 7 | |a Natural language & machine translation. |2 bicssc | |
| 650 | 7 | |a Neural networks & fuzzy systems. |2 bicssc | |
| 650 | 7 | |a Artificial intelligence. |2 bicssc | |
| 650 | 7 | |a Computers |x Natural Language Processing. |2 bisacsh | |
| 650 | 7 | |a Computers |x Neural Networks. |2 bisacsh | |
| 650 | 7 | |a Computers |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a Artificial intelligence |2 fast | |
| 700 | 1 | |a Shanmugamani, rajalingappaa. | |
| 700 | 1 | |a Boschetti, Alberto. | |
| 700 | 1 | |a Massaron, Luca. | |
| 700 | 1 | |a Thakur, Abhishek. | |
| 758 | |i has work: |a TensorFlow deep learning projects (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGwR8yDX3gqX8r9wX9y6Dm |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Grigorev, Alexey. |t TensorFlow Deep Learning Projects : 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning. |d Birmingham : Packt Publishing, ©2018 |
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| _version_ | 1816882417960484864 |
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| author | Grigorev, Alexey |
| author2 | Shanmugamani, rajalingappaa Boschetti, Alberto Massaron, Luca Thakur, Abhishek |
| author2_role | |
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| contents | Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Recognizing traffic signs using Convnets; The dataset; The CNN network; Image preprocessing; Train the model and make predictions; Follow-up questions; Summary; Chapter 2: Annotating Images with Object Detection API; The Microsoft common objects in context; The TensorFlow object detection API; Grasping the basics of R-CNN, R-FCN and SSD models; Presenting our project plan; Setting up an environment suitable for the project; Protobuf compilation; Windows installation; Unix installation. Provisioning of the project codeSome simple applications; Real-time webcam detection; Acknowledgements; Summary; Chapter 3: Caption Generation for Images; What is caption generation?; Exploring image captioning datasets; Downloading the dataset; Converting words into embeddings; Image captioning approaches; Conditional random field; Recurrent neural network on convolution neural network; Caption ranking; Dense captioning; RNN captioning; Multimodal captioning; Attention-based captioning; Implementing a caption generation model; Summary; Chapter 4: Building GANs for Conditional Image Creation. Introducing GANsThe key is in the adversarial approach; A cambrian explosion; DCGANs; Conditional GANs; The project; Dataset class; CGAN class; Putting CGAN to work on some examples; MNIST; Zalando MNIST; EMNIST; Reusing the trained CGANs; Resorting to Amazon Web Service; Acknowledgements; Summary; Chapter 5: Stock Price Prediction with LSTM; Input datasets -- cosine and stock price; Format the dataset; Using regression to predict the future prices of a stock; Long short-term memory -- LSTM 101; Stock price prediction with LSTM; Possible follow -- up questions; Summary. Chapter 6: Create and Train Machine Translation SystemsA walkthrough of the architecture; Preprocessing of the corpora; Training the machine translator; Test and translate; Home assignments; Summary; Chapter 7: Train and Set up a Chatbot, Able to Discuss Like a Human; Introduction to the project; The input corpus; Creating the training dataset; Training the chatbot; Chatbox API; Home assignments; Summary; Chapter 8: Detecting Duplicate Quora Questions; Presenting the dataset; Starting with basic feature engineering; Creating fuzzy features; Resorting to TF-IDF and SVD features. Mapping with Word2vec embeddingsTesting machine learning models; Building a TensorFlow model; Processing before deep neural networks; Deep neural networks building blocks; Designing the learning architecture; Summary; Chapter 9: Building a TensorFlow Recommender System; Recommender systems; Matrix factorization for recommender systems; Dataset preparation and baseline; Matrix factorization; Implicit feedback datasets; SGD-based matrix factorization; Bayesian personalized ranking; RNN for recommender systems; Data preparation and baseline; RNN recommender system in TensorFlow; Summary. |
| ctrlnum | (OCoLC)1030816734 |
| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
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| dewey-sort | 16.3 |
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| discipline | Informatik |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:28:17Z |
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| publisher | Packt Publishing, |
| record_format | marc |
| spelling | Grigorev, Alexey. TensorFlow Deep Learning Projects : 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning. Birmingham : Packt Publishing, 2018. 1 online resource (310 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Recognizing traffic signs using Convnets; The dataset; The CNN network; Image preprocessing; Train the model and make predictions; Follow-up questions; Summary; Chapter 2: Annotating Images with Object Detection API; The Microsoft common objects in context; The TensorFlow object detection API; Grasping the basics of R-CNN, R-FCN and SSD models; Presenting our project plan; Setting up an environment suitable for the project; Protobuf compilation; Windows installation; Unix installation. Provisioning of the project codeSome simple applications; Real-time webcam detection; Acknowledgements; Summary; Chapter 3: Caption Generation for Images; What is caption generation?; Exploring image captioning datasets; Downloading the dataset; Converting words into embeddings; Image captioning approaches; Conditional random field; Recurrent neural network on convolution neural network; Caption ranking; Dense captioning; RNN captioning; Multimodal captioning; Attention-based captioning; Implementing a caption generation model; Summary; Chapter 4: Building GANs for Conditional Image Creation. Introducing GANsThe key is in the adversarial approach; A cambrian explosion; DCGANs; Conditional GANs; The project; Dataset class; CGAN class; Putting CGAN to work on some examples; MNIST; Zalando MNIST; EMNIST; Reusing the trained CGANs; Resorting to Amazon Web Service; Acknowledgements; Summary; Chapter 5: Stock Price Prediction with LSTM; Input datasets -- cosine and stock price; Format the dataset; Using regression to predict the future prices of a stock; Long short-term memory -- LSTM 101; Stock price prediction with LSTM; Possible follow -- up questions; Summary. Chapter 6: Create and Train Machine Translation SystemsA walkthrough of the architecture; Preprocessing of the corpora; Training the machine translator; Test and translate; Home assignments; Summary; Chapter 7: Train and Set up a Chatbot, Able to Discuss Like a Human; Introduction to the project; The input corpus; Creating the training dataset; Training the chatbot; Chatbox API; Home assignments; Summary; Chapter 8: Detecting Duplicate Quora Questions; Presenting the dataset; Starting with basic feature engineering; Creating fuzzy features; Resorting to TF-IDF and SVD features. Mapping with Word2vec embeddingsTesting machine learning models; Building a TensorFlow model; Processing before deep neural networks; Deep neural networks building blocks; Designing the learning architecture; Summary; Chapter 9: Building a TensorFlow Recommender System; Recommender systems; Matrix factorization for recommender systems; Dataset preparation and baseline; Matrix factorization; Implicit feedback datasets; SGD-based matrix factorization; Bayesian personalized ranking; RNN for recommender systems; Data preparation and baseline; RNN recommender system in TensorFlow; Summary. Chapter 10: Video Games by Reinforcement Learning. This book is your guide to master deep learning with TensorFlow, with the help of 10 real-world projects. You will train high-performance models in TensorFlow to generate captions for images automatically, predict stocks' performance, create intelligent chatbots, perform large-scale text classification, develop recommendation systems, and more. Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Intelligence artificielle. artificial intelligence. aat Natural language & machine translation. bicssc Neural networks & fuzzy systems. bicssc Artificial intelligence. bicssc Computers Natural Language Processing. bisacsh Computers Neural Networks. bisacsh Computers Intelligence (AI) & Semantics. bisacsh COMPUTERS General. bisacsh Artificial intelligence fast Shanmugamani, rajalingappaa. Boschetti, Alberto. Massaron, Luca. Thakur, Abhishek. has work: TensorFlow deep learning projects (Text) https://id.oclc.org/worldcat/entity/E39PCGwR8yDX3gqX8r9wX9y6Dm https://id.oclc.org/worldcat/ontology/hasWork Print version: Grigorev, Alexey. TensorFlow Deep Learning Projects : 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning. Birmingham : Packt Publishing, ©2018 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1775079 Volltext |
| spellingShingle | Grigorev, Alexey TensorFlow Deep Learning Projects : 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning. Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Recognizing traffic signs using Convnets; The dataset; The CNN network; Image preprocessing; Train the model and make predictions; Follow-up questions; Summary; Chapter 2: Annotating Images with Object Detection API; The Microsoft common objects in context; The TensorFlow object detection API; Grasping the basics of R-CNN, R-FCN and SSD models; Presenting our project plan; Setting up an environment suitable for the project; Protobuf compilation; Windows installation; Unix installation. Provisioning of the project codeSome simple applications; Real-time webcam detection; Acknowledgements; Summary; Chapter 3: Caption Generation for Images; What is caption generation?; Exploring image captioning datasets; Downloading the dataset; Converting words into embeddings; Image captioning approaches; Conditional random field; Recurrent neural network on convolution neural network; Caption ranking; Dense captioning; RNN captioning; Multimodal captioning; Attention-based captioning; Implementing a caption generation model; Summary; Chapter 4: Building GANs for Conditional Image Creation. Introducing GANsThe key is in the adversarial approach; A cambrian explosion; DCGANs; Conditional GANs; The project; Dataset class; CGAN class; Putting CGAN to work on some examples; MNIST; Zalando MNIST; EMNIST; Reusing the trained CGANs; Resorting to Amazon Web Service; Acknowledgements; Summary; Chapter 5: Stock Price Prediction with LSTM; Input datasets -- cosine and stock price; Format the dataset; Using regression to predict the future prices of a stock; Long short-term memory -- LSTM 101; Stock price prediction with LSTM; Possible follow -- up questions; Summary. Chapter 6: Create and Train Machine Translation SystemsA walkthrough of the architecture; Preprocessing of the corpora; Training the machine translator; Test and translate; Home assignments; Summary; Chapter 7: Train and Set up a Chatbot, Able to Discuss Like a Human; Introduction to the project; The input corpus; Creating the training dataset; Training the chatbot; Chatbox API; Home assignments; Summary; Chapter 8: Detecting Duplicate Quora Questions; Presenting the dataset; Starting with basic feature engineering; Creating fuzzy features; Resorting to TF-IDF and SVD features. Mapping with Word2vec embeddingsTesting machine learning models; Building a TensorFlow model; Processing before deep neural networks; Deep neural networks building blocks; Designing the learning architecture; Summary; Chapter 9: Building a TensorFlow Recommender System; Recommender systems; Matrix factorization for recommender systems; Dataset preparation and baseline; Matrix factorization; Implicit feedback datasets; SGD-based matrix factorization; Bayesian personalized ranking; RNN for recommender systems; Data preparation and baseline; RNN recommender system in TensorFlow; Summary. Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Intelligence artificielle. artificial intelligence. aat Natural language & machine translation. bicssc Neural networks & fuzzy systems. bicssc Artificial intelligence. bicssc Computers Natural Language Processing. bisacsh Computers Neural Networks. bisacsh Computers Intelligence (AI) & Semantics. bisacsh COMPUTERS General. bisacsh Artificial intelligence fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85008180 |
| title | TensorFlow Deep Learning Projects : 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning. |
| title_auth | TensorFlow Deep Learning Projects : 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning. |
| title_exact_search | TensorFlow Deep Learning Projects : 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning. |
| title_full | TensorFlow Deep Learning Projects : 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning. |
| title_fullStr | TensorFlow Deep Learning Projects : 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning. |
| title_full_unstemmed | TensorFlow Deep Learning Projects : 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning. |
| title_short | TensorFlow Deep Learning Projects : |
| title_sort | tensorflow deep learning projects 10 real world projects on computer vision machine translation chatbots and reinforcement learning |
| title_sub | 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning. |
| topic | Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Intelligence artificielle. artificial intelligence. aat Natural language & machine translation. bicssc Neural networks & fuzzy systems. bicssc Artificial intelligence. bicssc Computers Natural Language Processing. bisacsh Computers Neural Networks. bisacsh Computers Intelligence (AI) & Semantics. bisacsh COMPUTERS General. bisacsh Artificial intelligence fast |
| topic_facet | Artificial intelligence. Intelligence artificielle. artificial intelligence. Natural language & machine translation. Neural networks & fuzzy systems. Computers Natural Language Processing. Computers Neural Networks. Computers Intelligence (AI) & Semantics. COMPUTERS General. Artificial intelligence |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1775079 |
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