Deep Learning for Data Architects Book [PDF] Download

Download the fantastic book titled Deep Learning for Data Architects written by Shekhar Khandelwal, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "Deep Learning for Data Architects", which was released on 16 August 2023. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Computers genre.

Summary of Deep Learning for Data Architects by Shekhar Khandelwal PDF

A 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


Detail About Deep Learning for Data Architects PDF

  • Author : Shekhar Khandelwal
  • Publisher : BPB Publications
  • Genre : Computers
  • Total Pages : 251 pages
  • ISBN : 9355515391
  • PDF File Size : 37,6 Mb
  • Language : English
  • Rating : 4/5 from 21 reviews

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Deep Learning for Data Architects

Deep Learning for Data Architects
  • Publisher : BPB Publications
  • File Size : 43,6 Mb
  • Release Date : 16 August 2023
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A 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

The Machine Learning Solutions Architect Handbook

The Machine Learning Solutions Architect Handbook
  • Publisher : Packt Publishing Ltd
  • File Size : 54,8 Mb
  • Release Date : 21 January 2022
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Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions Key Features Explore different ML tools and frameworks to solve large-scale machine learning

Deep Learning for Computer Architects

Deep Learning for Computer Architects
  • Publisher : Springer Nature
  • File Size : 49,6 Mb
  • Release Date : 31 May 2022
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Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems

The Deep Learning Architect's Handbook

The Deep Learning Architect's Handbook
  • Publisher : Packt Publishing Ltd
  • File Size : 52,7 Mb
  • Release Date : 29 December 2023
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Harness the power of deep learning to drive productivity and efficiency using this practical guide covering techniques and best practices for the entire deep learning life cycle Key Features Interpret

Machine Learning

Machine Learning
  • Publisher : Routledge
  • File Size : 48,8 Mb
  • Release Date : 30 April 2022
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‘The advent of machine learning-based AI systems demands that our industry does not just share toys, but builds a new sandbox in which to play with them.’ - Phil Bernstein

Deep Learning in Data Analytics

Deep Learning in Data Analytics
  • Publisher : Springer Nature
  • File Size : 42,6 Mb
  • Release Date : 11 August 2021
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This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning

Big Data Architect’s Handbook

Big Data Architect’s Handbook
  • Publisher : Packt Publishing Ltd
  • File Size : 43,7 Mb
  • Release Date : 21 June 2018
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A comprehensive end-to-end guide that gives hands-on practice in big data and Artificial Intelligence Key Features Learn to build and run a big data application with sample code Explore examples

Data Orchestration in Deep Learning Accelerators

Data Orchestration in Deep Learning Accelerators
  • Publisher : Springer Nature
  • File Size : 46,8 Mb
  • Release Date : 31 May 2022
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This Synthesis Lecture focuses on techniques for efficient data orchestration within DNN accelerators. The End of Moore's Law, coupled with the increasing growth in deep learning and other AI applications

Deep Learning: Convergence to Big Data Analytics

Deep Learning: Convergence to Big Data Analytics
  • Publisher : Springer
  • File Size : 54,7 Mb
  • Release Date : 30 December 2018
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This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used

Scalable Big Data Architecture

Scalable Big Data Architecture
  • Publisher : Apress
  • File Size : 36,5 Mb
  • Release Date : 31 December 2015
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This book highlights the different types of data architecture and illustrates the many possibilities hidden behind the term "Big Data", from the usage of No-SQL databases to the deployment of