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1. |
Record Nr. |
UNINA990009718500403321 |
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Autore |
Cuaz, Marco <1953- > |
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Titolo |
Alle frontiere dello Stato : la scuola elementare in Valle d'Aosta dalla restaurazione al fascismo / Marco Cuaz |
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Descrizione fisica |
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Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNINA9910483235303321 |
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Autore |
Paper David |
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Titolo |
TensorFlow 2.x in the Colaboratory Cloud : an introduction to deep learning on Google's Cloud Service / / David Paper |
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Pubbl/distr/stampa |
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[Place of publication not identified] : , : Apress, , [2021] |
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©2021 |
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ISBN |
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Edizione |
[1st ed. 2021.] |
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Descrizione fisica |
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1 online resource (XXIII, 264 p. 5 illus.) |
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Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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1. Introduction to Deep Learning -- 2. Build Your First Neural Network with Google Colab -- 3. Working with TensorFlow Data -- 4. Working with Other Data -- 5. Classification -- 6. Regression -- 7. Convolutional Neural Networks -- 8. Automated Text Generation -- 9. Sentiment Analysis -- 10. Time Series Forecasting with RNNs. |
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Sommario/riassunto |
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Use TensorFlow 2.x with Google's Colaboratory (Colab) product that offers a free cloud service for Python programmers. Colab is especially well suited as a platform for TensorFlow 2.x deep learning applications. You will learn Colab’s default install of the most current TensorFlow 2.x along with Colab’s easy access to on-demand GPU hardware acceleration in the cloud for fast execution of deep learning models. This book offers you the opportunity to grasp deep learning in an applied manner with the only requirement being an Internet connection. Everything else—Python, TensorFlow 2.x, GPU support, and Jupyter Notebooks—is provided and ready to go from Colab. The book begins with an introduction to TensorFlow 2.x and the Google Colab cloud service. You will learn how to provision a workspace on Google Colab and build a simple neural network application. From there you will progress into TensorFlow datasets and building input pipelines in support of modeling and testing. You will find coverage of deep learning classification and regression, with clear code examples showing how to perform each of those functions. Advanced topics covered in the book include convolutional neural networks and recurrent neural networks. This book contains all the applied math and programming you need to master the content. Examples range from simple to relatively complex when necessary to ensure acquisition of appropriate deep learning concepts and constructs. Examples are carefully explained, concise, accurate, and complete to perfectly complement deep learning skill development. Care is taken to walk you through the foundational principles of deep learning through clear examples written in Python that you can try out and experiment with using Google Colab from the comfort of your own home or office. You will: Be familiar with the basic concepts and constructs of applied deep learning Create machine learning models with clean and reliable Python code Work with datasets common to deep learning applications Prepare data for TensorFlow consumption Take advantage of Google Colab’s built-in support for deep learning Execute deep learning experiments using a variety of neural network models Be able to mount Google Colab directly to your Google Drive account Visualize training versus test performance to see model fit. |
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