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Programming with TensorFlow : solution for edge computing applications / / Kolla Bhanu Prakash, G. R. Kanagachidambaresan, editors
| Programming with TensorFlow : solution for edge computing applications / / Kolla Bhanu Prakash, G. R. Kanagachidambaresan, editors |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (X, 190 p.) |
| Disciplina | 006.32 |
| Collana | EAI/Springer Innovations in Communication and Computing |
| Soggetto topico |
Neural networks (Computer science)
Machine learning TensorFlow |
| ISBN | 3-030-57077-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Installation Guide to Tensorflow -- Hello Tensorflow Program -- Representation of Vector -- Session with Tensorflow -- Matrix elementary operation -- Variable and constant -- Simple mathematical operation -- Matrix -- Variable Concept & Implementation -- Placeholder Concept & Implementation -- Equation with Tensor -- Matplot -- Regression Model -- Neural Network -- Convolutional Neural Network -- Recurrent Neural Network -- Application of Machine Learning & Deep Learning -- Implementing Chatbots -- Working with Text and Sequences + TensorBoard visualization -- TensorFlow Autoencoders -- Advanced TensorFlow Programming -- Reinforcement Learning -- RNN & LSTM using Keras -- Deep Learning with Pytorch -- Conclusion. |
| Record Nr. | UNINA-9910483166903321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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System design for epidemics using machine learning and deep learning / / G.R. Kanagachidambaresan, Dinesh Bhatia, Dhilip Kumar, Animesh Mishra, editors
| System design for epidemics using machine learning and deep learning / / G.R. Kanagachidambaresan, Dinesh Bhatia, Dhilip Kumar, Animesh Mishra, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (336 pages) |
| Disciplina | 610.28563 |
| Collana | Signals and communication technology |
| Soggetto topico |
Artificial intelligence - Medical applications
Deep learning (Machine learning) - Therapeutic use Epidemics - Prevention - Technological innovations Deep learning (Machine learning) Epidemics - Prevention Deep Learning Machine Learning Epidemics - prevention & control Electronic Data Processing - methods |
| ISBN | 3-031-19752-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Pandemic effect of COVID-19: Identification, Present scenario and preventive measures using Machine learning model. -- 2. A Comprehensive Review of the Smart Health Records to prevent Pandemic -- 3. Automation of COVID-19 Disease Diagnosis from Radiograph -- 4. Applications of Artificial Intelligence in the attainment of Sustainable Development Goals -- 5. A Novel Model for IoT Blockchain Assurance Based Compliance to COVID Quarantine -- 6. DEEP LEARNING BASED CONVOLUTIONALNEURAL NETWORK WITH RANDOM FOREST APPROACH FOR MRI BRAIN TUMOUR SEGMENTATION -- 7. Expert systems for improving the effectiveness of remote health monitoring in Covid-19 Pandemic -- A Critical Review -- 8. Artificial Intelligence-based predictive tools for Life-threatening diseases -- 9. Deep Convolutional Generative Adversarial Network for Metastatic Tissue Diagnosis in Lymph Node Section -- 10. Transformation in Health Sector during Pandemic by Photonics Devices -- 11. DIAGNOSIS OF COVID-19 FROM CT IMAGES AND RESPIRATORY SOUND SIGNALS USING DEEP LEARNING STRATEGIES -- 12. The Role of Edge Computing in Pandemic and Epidemic Situations with its Solutions -- 13. Advances and application of Artificial Intelligence and Machine learning in the field of cardiovascular diseases and its role during the Pandemic condition -- 14. Effective Health Screening and Prompt Vaccination to Counter the Spread of Covid-19 and Minimize its Adverse Effects -- 15. CROWD DENSITY ESTIMATION USING NEURAL NETWORK FOR COVID'19 AND FUTURE PANDEMICS -- 16. "Role of digital healthcare in rehabilitation during pandemic" -- 17. AN EPIDEMIC OF NEURODEGENERATIVE DISEASE ANALYSIS USING MACHINE LEARNING TECHNIQUES -- 18. Covid-19 Growth Curve Forecasting for India using Deep Learning Techniques. |
| Record Nr. | UNINA-9910736996903321 |
| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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