Python deep learning cookbook :: over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python /
Solve different problems in modelling deep neural networks using Python, Tensorflow, and Keras with this practical guide About This Book Practical recipes on training different neural network models and tuning them for optimal performance Use Python frameworks like TensorFlow, Caffe, Keras, Theano f...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
2017.
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| Online-Zugang: | Volltext |
| Zusammenfassung: | Solve different problems in modelling deep neural networks using Python, Tensorflow, and Keras with this practical guide About This Book Practical recipes on training different neural network models and tuning them for optimal performance Use Python frameworks like TensorFlow, Caffe, Keras, Theano for Natural Language Processing, Computer Vision, and more A hands-on guide covering the common as well as the not so common problems in deep learning using Python Who This Book Is For This book is intended for machine learning professionals who are looking to use deep learning algorithms to create real-world applications using Python. Thorough understanding of the machine learning concepts and Python libraries such as NumPy, SciPy and scikit-learn is expected. Additionally, basic knowledge in linear algebra and calculus is desired. What You Will Learn Implement different neural network models in Python Select the best Python framework for deep learning such as PyTorch, Tensorflow, MXNet and Keras Apply tips and tricks related to neural networks internals, to boost learning performances Consolidate machine learning principles and apply them in the deep learning field Reuse and adapt Python code snippets to everyday problems Evaluate the cost/benefits and performance implication of each discussed solution In Detail Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios. Style and approach Unique blend of independent recipes arranged in the most logical manner. |
| Beschreibung: | 1 online resource (1 volume) : illustrations |
| ISBN: | 9781787122253 1787122255 178712519X 9781787125193 |
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| 520 | |a Solve different problems in modelling deep neural networks using Python, Tensorflow, and Keras with this practical guide About This Book Practical recipes on training different neural network models and tuning them for optimal performance Use Python frameworks like TensorFlow, Caffe, Keras, Theano for Natural Language Processing, Computer Vision, and more A hands-on guide covering the common as well as the not so common problems in deep learning using Python Who This Book Is For This book is intended for machine learning professionals who are looking to use deep learning algorithms to create real-world applications using Python. Thorough understanding of the machine learning concepts and Python libraries such as NumPy, SciPy and scikit-learn is expected. Additionally, basic knowledge in linear algebra and calculus is desired. What You Will Learn Implement different neural network models in Python Select the best Python framework for deep learning such as PyTorch, Tensorflow, MXNet and Keras Apply tips and tricks related to neural networks internals, to boost learning performances Consolidate machine learning principles and apply them in the deep learning field Reuse and adapt Python code snippets to everyday problems Evaluate the cost/benefits and performance implication of each discussed solution In Detail Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios. Style and approach Unique blend of independent recipes arranged in the most logical manner. | ||
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| spelling | Bakker, Indra den, author. Python deep learning cookbook : over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python / Indra den Bakker. Over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python Birmingham, UK : Packt Publishing, 2017. 1 online resource (1 volume) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier data file Online resource; title from title page (Safari, viewed December 11, 2017). Solve different problems in modelling deep neural networks using Python, Tensorflow, and Keras with this practical guide About This Book Practical recipes on training different neural network models and tuning them for optimal performance Use Python frameworks like TensorFlow, Caffe, Keras, Theano for Natural Language Processing, Computer Vision, and more A hands-on guide covering the common as well as the not so common problems in deep learning using Python Who This Book Is For This book is intended for machine learning professionals who are looking to use deep learning algorithms to create real-world applications using Python. Thorough understanding of the machine learning concepts and Python libraries such as NumPy, SciPy and scikit-learn is expected. Additionally, basic knowledge in linear algebra and calculus is desired. What You Will Learn Implement different neural network models in Python Select the best Python framework for deep learning such as PyTorch, Tensorflow, MXNet and Keras Apply tips and tricks related to neural networks internals, to boost learning performances Consolidate machine learning principles and apply them in the deep learning field Reuse and adapt Python code snippets to everyday problems Evaluate the cost/benefits and performance implication of each discussed solution In Detail Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios. Style and approach Unique blend of independent recipes arranged in the most logical manner. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Computer programming. http://id.loc.gov/authorities/subjects/sh85107310 Python (Langage de programmation) Apprentissage automatique. Programmation (Informatique) computer programming. aat COMPUTERS Programming Languages Python. bisacsh Computer programming fast Machine learning fast Python (Computer program language) fast has work: Python Deep Learning Cookbook (Text) https://id.oclc.org/worldcat/entity/E39PCYpC9ykjMvddTWFCVyYrRq https://id.oclc.org/worldcat/ontology/hasWork FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1626955 Volltext CBO01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1626955 Volltext |
| spellingShingle | Bakker, Indra den Python deep learning cookbook : over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python / Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Computer programming. http://id.loc.gov/authorities/subjects/sh85107310 Python (Langage de programmation) Apprentissage automatique. Programmation (Informatique) computer programming. aat COMPUTERS Programming Languages Python. bisacsh Computer programming fast Machine learning fast Python (Computer program language) fast |
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| title | Python deep learning cookbook : over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python / |
| title_alt | Over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python |
| title_auth | Python deep learning cookbook : over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python / |
| title_exact_search | Python deep learning cookbook : over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python / |
| title_full | Python deep learning cookbook : over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python / Indra den Bakker. |
| title_fullStr | Python deep learning cookbook : over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python / Indra den Bakker. |
| title_full_unstemmed | Python deep learning cookbook : over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python / Indra den Bakker. |
| title_short | Python deep learning cookbook : |
| title_sort | python deep learning cookbook over 75 practical recipes on neural network modeling reinforcement learning and transfer learning using python |
| title_sub | over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python / |
| topic | Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Computer programming. http://id.loc.gov/authorities/subjects/sh85107310 Python (Langage de programmation) Apprentissage automatique. Programmation (Informatique) computer programming. aat COMPUTERS Programming Languages Python. bisacsh Computer programming fast Machine learning fast Python (Computer program language) fast |
| topic_facet | Python (Computer program language) Machine learning. Computer programming. Python (Langage de programmation) Apprentissage automatique. Programmation (Informatique) computer programming. COMPUTERS Programming Languages Python. Computer programming Machine learning |
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