000			03606cam a2200277zu 4500
001			88946819
003			FRCYB88946819
005			20250108002505.0
006			m o d
007			cr un
008			250108s2023 fr | o|||||0|0|||eng d
020			_a9789355515391
035			_aFRCYB88946819
040			_aFR-PaCSA _ben _c _erda
100	1		_aKhandelwal, Shekhar
245	0	1	_aDeep Learning for Data Architects _bUnleash the power of Python's deep learning algorithms (English Edition) _c['Khandelwal, Shekhar']
264		1	_bBPB Publications _c2023
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aKhandelwal, Shekhar
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88946819 _qtext/html _a
520			_aA hands-on guide to building and deploying deep learning models with Python Key Features ? Acquire the skills to perform exploratory data analysis, uncover insights, and preprocess data for deep learning tasks. ? Build and train various types of neural networks, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). ? Gain hands-on experience by working on practical projects and applying deep learning techniques to real-world problems. Description “Deep Learning for Data Architects” is a comprehensive guide that bridges the gap between data architecture and deep learning. It provides a solid foundation in Python for data science and serves as a launchpad into the world of AI and deep learning. The book begins by addressing the challenges of transforming raw data into actionable insights. It provides a practical understanding of data handling and covers the construction of neural network-based predictive models. The book then explores specialized networks such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). The book delves into the theory and practical aspects of these networks and offers Python code implementations for each. The final chapter of the book introduces Transformers, a revolutionary model that has had a significant impact on natural language processing (NLP). This chapter provides you with a thorough understanding of how Transformers work and includes Python code implementations. By the end of the book, you will be able to use deep learning to solve real-world problems. What you will learn ? Develop a comprehensive understanding of neural networks' key concepts and principles. ? Gain proficiency in Python as you code and implement major deep-learning algorithms from scratch. ? Build and implement predictive models using various neural networks ? Learn how to use Transformers for complex NLP tasks ? Explore techniques to enhance the performance of your deep learning models. Who this book is for This book is for anyone who is interested in a career in emerging technologies, such as artificial intelligence (AI), data analytics, machine learning, deep learning, and data science. It is a comprehensive guide that covers the fundamentals of these technologies, as well as the skills and knowledge that you need to succeed in this field. Table of Contents 1. Python for Data Science 2. Real-World Challenges for Data Professionals in Converting Data Into Insights 3. Build a Neural Network-Based Predictive Model 4. Convolutional Neural Networks 5. Optical Character Recognition 6. Object Detection 7. Image Segmentation 8. Recurrent Neural Networks 9. Generative Adversarial Networks 10. Transformers
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