English
- استرجاع مجاني وسهل
- أفضل العروض
Learn Keras For Deep Neural Networks: A Fast-Track Approach To Modern Deep Learning With Python paperback english - 2018-12-07
الآن:
192.25 د.إ.شامل ضريبة القيمة المضافة
احصل عليه خلال 7 ديسمبر
ادفع على 4 دفعات بدون فوائد بقيمة ٤٨٫٠٦ د.إ.اعرف المزيد
قسمها على 4 دفعات ب ٤٨٫٠٦ د.إ. بدون فوائد أو رسوم تأخير.اعرف المزيد
1
توصيل مجاني لنقطة نون ومراكز الاستلام
معرفة المزيد
إرجاع سهل لكل المنتجات في هذا العرض.
المنتج كما في الوصف
50%
شريك لنون منذ
2+ سنين
أحدث التقييمات الإيجابية
المواصفات
| الناشر | aPress |
| رقم الكتاب المعياري الدولي 13 | 9781484242391 |
| رقم الكتاب المعياري الدولي 10 | 1484242394 |
| اللغة | الإنجليزية |
| العنوان الفرعي للكتاب | A Fast-Track Approach To Modern Deep Learning With Python |
| وصف الكتاب | Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You'll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you'll further hone your skills in deep learning and cover areas of active development and research in deep learning. At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras. What You'll Learn Master fast-paced practical deep learning concepts with math- and programming-friendly abstractions. Design, develop, train, validate, and deploy deep neural networks using the Keras framework Use best practices for debugging and validating deep learning models Deploy and integrate deep learning as a service into a larger software service or product Extend deep learning principles into other popular frameworks Who This Book Is For Software engineers and data engineers with basic programming skills in any language and who are keen on exploring deep learning for a career move or an enterprise project. |
| عن المؤلف | Jojo Moolayil is an artificial intelligence, deep learning, machine learning, and decision science professional with over five years of industrial experience and is a published author of the book Smarter Decisions - The Intersection of IoT and Decision Science. He has worked with several industry leaders on high-impact and critical data science and machine learning projects across multiple verticals. He was born and raised in Pune, India and graduated from the University of Pune with a major in Information Technology Engineering. He started his career with Mu Sigma Inc., the world's largest pure-play analytics provider and worked with the leaders of many Fortune 50 clients. He later worked with Flutura - an IoT analytics startup and GE. He currently resides in Vancouver, BC. Apart from writing books on decision science and IoT, Jojo has also been a technical reviewer for various books on machine learning, deep learning and business analytics with Apress and Packt publications. He is an active data science tutor. |
| رقم الطبعة | 1 |
| تاريخ النشر | 2018-12-07 |
| عدد الصفحات | 182 |
Learn Keras For Deep Neural Networks: A Fast-Track Approach To Modern Deep Learning With Python paperback english - 2018-12-07
تمت الإضافة لعربة التسوق
مجموع السلة 192.25 د.إ.
