Deep Learning Pipeline: Building a Deep Learning Model with TensorFlow
Autor Hisham El-Amir, Mahmoud Hamdyen Limba Engleză Paperback – 21 dec 2019
You'll learn what a pipeline is and how it works so you can build a full application easily and rapidly. Then troubleshoot and overcome basic Tensorflow obstacles to easily create functional apps and deploy well-trained models. Step-by-step and example-oriented instructions help you understand each step of the deep learning pipeline while you apply the most straightforward and effective tools to demonstrative problems and datasets.
You'll also develop a deep learning project by preparing data, choosing the model that fits that data, and debugging your model to get the best fit to data all using Tensorflow techniques. Enhance your skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution or entering a Kaggle contest, Deep Learning Pipeline is for you!
What You'll Learn
- Develop a deep learning project using data
- Study and apply various models to your data
- Debug and troubleshoot the proper model suited for your data
Who This Book Is For
Developers, analysts, and data scientists looking to add to or enhance their existing skills by accessing some of the most powerful recent trends in data science. Prior experience in Python or other TensorFlow related languages and mathematics would be helpful.
Preț: 230.79 lei
Preț vechi: 288.49 lei
-20% Nou
Puncte Express: 346
Preț estimativ în valută:
44.17€ • 45.94$ • 36.96£
44.17€ • 45.94$ • 36.96£
Carte disponibilă
Livrare economică 21 februarie-07 martie
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9781484253489
ISBN-10: 1484253485
Pagini: 551
Ilustrații: XXV, 551 p. 214 illus.
Dimensiuni: 155 x 235 mm
Greutate: 0.8 kg
Ediția:1st ed.
Editura: Apress
Colecția Apress
Locul publicării:Berkeley, CA, United States
ISBN-10: 1484253485
Pagini: 551
Ilustrații: XXV, 551 p. 214 illus.
Dimensiuni: 155 x 235 mm
Greutate: 0.8 kg
Ediția:1st ed.
Editura: Apress
Colecția Apress
Locul publicării:Berkeley, CA, United States
V-ar putea interesa
-
-39%Preț: 679.00 lei1114.54 lei -
-20%Preț: 1738.41 lei2173.02 lei -
An Introduction to MultiAgent Systems 2eM. Wooldridge-20%Preț: 414.67 lei518.33 lei -
Artificial IntelligenceM. Tim Jones-20%Preț: 1108.40 lei1385.50 lei -
Practical Applications of Data MiningSang C. Suh-20%Preț: 788.91 lei986.14 lei -
Thinking with DiagramsAlan F. Blackwell-20%Preț: 928.43 lei1160.54 lei -
Logic-Based Artificial IntelligenceJack Minker-20%Preț: 1465.45 lei1831.82 lei -
Entertainment Computing: Technologies and ApplicationRyohei Nakatsu-20%Preț: 660.16 lei825.20 lei -
A Guide to Artificial Intelligence with Visual PROLOGRandall Scott-20%Preț: 122.79 lei153.48 lei -
Artificial IntelligenceRandal B. Bernstein-38%Preț: 867.62 lei1403.20 lei -
-20%Preț: 1231.13 lei1538.90 lei -
Handbook of Research on Multi-Agent SystemsVirginia Dignum-20%Preț: 1668.20 lei2085.25 lei -
-20%Preț: 1134.39 lei1417.98 lei -
-20%Preț: 1127.81 lei1409.75 lei -
Particle Swarm Optimization and IntelligenceKonstantinos E. Parsopoulos-23%Preț: 1013.41 lei1316.11 lei -
-23%Preț: 999.33 lei1297.82 lei -
Soft Computing Methods for Practical Environment SolutionsDaniel Rivero Cebrian-23%Preț: 1023.93 lei1329.78 lei -
Machine AuditionWenwu Wang-23%Preț: 1025.86 lei1332.28 lei -
Particle Swarm OptimizationAndrea E. Olsson-38%Preț: 1079.47 lei1753.19 lei -
Nanotechnology and MicroelectronicsNdubuisi Ekekwe-23%Preț: 1025.41 lei1331.69 lei -
Affective Computing and InteractionDidem Gkay-23%Preț: 1018.73 lei1323.03 lei -
Spoken Dialogue Technology: Toward the Conversational User InterfaceMichael F. McTear-20%Preț: 339.66 lei424.58 lei -
-20%Preț: 329.76 lei412.21 lei -
-20%Preț: 665.43 lei831.78 lei -
-20%Preț: 671.85 lei839.82 lei
Cuprins
Deep Learning Pipeline
Notă biografică
Hisham Elamir is a data scientist with expertise in machine learning, deep learning, and statistics. He currently lives and works in Cairo, Egypt. In his work projects, he faces challenges ranging from natural language processing (NLP), behavioral analysis, and machine learning to distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meetups, conferences, and other events.
Mahmoud Hamdy is a machine learning engineer who works in Egypt and lives in Egypt, His primary area of study is the overlap between knowledge, logic, language, and learning. He works helping train machine learning, and deep learning models to distil large amounts of unstructured, semi-structured, and structured data into new knowledge about the world by using methods ranging from deep learning to statistical relational learning. He applies strong theoretical and practical skills in several areas of machine learning to finding novel and effective solutions for interesting and challenging problems in such interconnections
Mahmoud Hamdy is a machine learning engineer who works in Egypt and lives in Egypt, His primary area of study is the overlap between knowledge, logic, language, and learning. He works helping train machine learning, and deep learning models to distil large amounts of unstructured, semi-structured, and structured data into new knowledge about the world by using methods ranging from deep learning to statistical relational learning. He applies strong theoretical and practical skills in several areas of machine learning to finding novel and effective solutions for interesting and challenging problems in such interconnections
Textul de pe ultima copertă
Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. This book shows you how to build a deep learning pipeline for real-life TensorFlow projects.
You'll learn what a pipeline is and how it works so you can build a full application easily and rapidly. Then troubleshoot and overcome basic Tensorflow obstacles to easily create functional apps and deploy well-trained models. Step-by-step and example-oriented instructions help you understand each step of the deep learning pipeline while you apply the most straightforward and effective tools to demonstrative problems and datasets.
You'll also develop a deep learning project by preparing data, choosing the model that fits that data, and debugging your model to get the best fit to data all using Tensorflow techniques. Enhance your skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution or entering a Kaggle contest, Deep Learning Pipeline is for you!
You'll learn what a pipeline is and how it works so you can build a full application easily and rapidly. Then troubleshoot and overcome basic Tensorflow obstacles to easily create functional apps and deploy well-trained models. Step-by-step and example-oriented instructions help you understand each step of the deep learning pipeline while you apply the most straightforward and effective tools to demonstrative problems and datasets.
You'll also develop a deep learning project by preparing data, choosing the model that fits that data, and debugging your model to get the best fit to data all using Tensorflow techniques. Enhance your skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution or entering a Kaggle contest, Deep Learning Pipeline is for you!
Caracteristici
Discover the difference between the regular and pipelined model of deep learning Learn all the detailed steps of an applicable deep learning pipeline Use a pipeline to help better manage deep learning projects and applications