Deep Learning For Finance (Record no. 359832)

MARC details
000 -LEADER
fixed length control field 07441 a2200157 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20241018124852.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 241018b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789355424976
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title English
100 ## - MAIN ENTRY--PERSONAL NAME
Author Kaabar S.
245 ## - TITLE STATEMENT
Title Deep Learning For Finance
Remainder of title :Creating Machine A Deep Learning Models For Trading In Python
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Name of publisher, distributor, etc. SPD
Date of publication, distribution, etc. 2024
300 ## - PHYSICAL DESCRIPTION
Extent 344
520 ## - SUMMARY, ETC.
Summary, etc. Table of Contents<br/>Foreword. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi<br/>Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii<br/>1. The Business Transformation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1<br/>Why Businesses Are Transforming 1<br/>Factor 1: The Speed of Change 2<br/>Factor 2: The Convergence of Multiple Technologies 2<br/>Factor 3: The Importance of Data 2<br/>Factor 4: Changing Consumer Behavior and Customer Centricity 4<br/>Industries Heavily Impacted by Digital Transformation 5<br/>The Consequences for Your Business 7<br/>There’s No Analytics Transformation Without Augmented Analytics 8<br/>A Data-Driven Culture 9<br/>The “People Problem” and the Limits of Upskilling 9<br/>Conclusion 10<br/>2. The Analytics Problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11<br/>Finding Your Analytics Purpose 12<br/>Competition and Customer Expectations 12<br/>Operational Efficiency 12<br/>Availability and User Friendliness 12<br/>Innovation 13<br/>Regulatory Compliance 13<br/>How to Start Your Analytics Journey 13<br/>Industry Examples 14<br/>Ecommerce 14<br/>Healthcare 14<br/>v<br/>Manufacturing 14<br/>Financial Services 14<br/>Government 15<br/>Commercial Insurance 15<br/>The Concept of Analytical Maturity 16<br/>Determine Your Current—and Future—Data Maturity 20<br/>Stage 1: Data Reactive 20<br/>Stage 2: Data Active 23<br/>Stage 3: Data Progressive 31<br/>Stage 4: Data Fluent 36<br/>Conclusion 40<br/>3. Understanding Augmented Analytics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41<br/>Definition 41<br/>The Five I’s of Augmented Analytics 43<br/>Overcoming the Limitations of Traditional Analytics Approaches 44<br/>Augmented Workflows 45<br/>The Benefits of Augmented Analytics 46<br/>AA Gives Nonexpert Users a Better Experience 48<br/>Automated Integration Provides More Complete Insights 49<br/>AA Gives Faster, More Efficient Insights 49<br/>Standardization Reduces Human Errors and Bias for Better Insights 50<br/>AA Tools Are Easier to Scale Up 51<br/>AA Reaches Further Afield to Generate Unexpected Insights 51<br/>Overcoming Bias 51<br/>Key Enablers of Augmented Analytics 55<br/>Automation and AI 56<br/>Artificial Intelligence: The Five Archetypes 57<br/>The Limitations of Augmented Analytics 64<br/>The Challenges of Augmented Analytics 66<br/>Conclusion 67<br/>4. Preparing People and the Organization for Augmented Analytics. . . . . . . . . . . . . . . . . . 69<br/>Tailoring Augmented Analytics for Different Organizational Roles 70<br/>Analytics Leader 71<br/>Analytics Translator 73<br/>Analytics User 74<br/>Analytics Professional 78<br/>Analytics Transformation Manager 82<br/>Summary of Key Roles 84<br/>vi | Table of Contents<br/>The Center of Excellence 86<br/>Creating a Center of Excellence 86<br/>Approaches to Organizing a CoE 89<br/>Driving Transformational Change with the Influence Model 94<br/>Fostering Understanding and Conviction 95<br/>Reinforcing with Formal Mechanisms 95<br/>Developing Talent and Skills 96<br/>Role Modeling 96<br/>Cultivating a Data-Literate Culture 97<br/>Cultivating Analytics Awareness 98<br/>Storytelling with Data 99<br/>Embracing Data-Driven Management 100<br/>Leading in the Age of AI 100<br/>The Enablement Program 101<br/>Training Formats for Analytics Leaders 101<br/>Training Formats for Analytics Translators 105<br/>Data Literacy Training 107<br/>Technical Training 107<br/>Conclusion 108<br/>5. Augmented Workflows. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109<br/>Types of Workflow Augmentation 110<br/>Fixed-Rule, High-Confidence Augmentation 110<br/>Idea and Insight Enrichment 111<br/>Conversational Augmentation 111<br/>Contextual Augmentation 112<br/>Collaborative Augmentation 112<br/>The Analytics Use-Case Approach: Finding Workflows to Augment 112<br/>Phase 1. Idea: The Initial Spark 114<br/>Phase 2. Concept: Structuring the Idea 115<br/>Phase 3. Proof of Concept: Testing the Waters 118<br/>Phase 4. Prototyping: Shaping the Concept 121<br/>Phase 5. Pilot: The Test Run 123<br/>Phase 6. Product: Full Deployment 124<br/>Making the Make-or-Buy Decision 124<br/>Decision Scenarios 127<br/>Overarching Success Factors 129<br/>Balancing Automation and Integration 130<br/>The Use-Case Library 133<br/>Technical Requirements for Implementing AA 139<br/>Table of Contents | vii<br/>Infrastructure Setup Challenges 141<br/>IT System Integration Challenges 149<br/>Governance Challenges 151<br/>Conclusion 157<br/>6. Augmented Frames. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159<br/>Business Objects and Frame Units 159<br/>Understanding Frames 162<br/>Key Features of Frames 164<br/>Frame Types 165<br/>Frame Engines 167<br/>Frame Engine Types 169<br/>Attribute Aggregation 170<br/>Engine Interfaces 172<br/>Result Objects 179<br/>Implementation Challenges 186<br/>Frame Agent 187<br/>Dissolving Frames 188<br/>Identifying Types 188<br/>Translating Frame Units 188<br/>Enriching Frames 188<br/>Orchestrating Calls 189<br/>Standardizing Results 189<br/>Central Repository 189<br/>Monitoring and Performance Analysis 189<br/>User Access and Security 190<br/>User Interface 190<br/>Frame Dissolver 193<br/>Frame Adapter 195<br/>Dealing with Group Variables 196<br/>Dealing with Bottom-up Business Object Structures 198<br/>Dealing with Unconnected Business Objects 199<br/>Frame Creator 200<br/>Case Study: AP/TP Frame Engine 201<br/>Infrastructure and Technology 207<br/>An Iterative Approach to Introducing Augmented Frames 211<br/>Iteration 1: Free Frames and Frame Engines 211<br/>Iteration 2: A Frame Agent and Frame Adapter 212<br/>Iteration 3: The Frame Dissolver, ID Frames, and Indexed Frames 213<br/>Iteration 4: Static Frames 213<br/>viii | Table of Contents<br/>Iteration 5: Dynamic Frames 214<br/>Iteration 6: The Frame Creator 215<br/>Iteration Wrap-up 215<br/>Conclusion 216<br/>7. Applied Examples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219<br/>The Underwriting Process 219<br/>Types of Augmented Workflows in Underwriting 220<br/>The Workflows in Detail 221<br/>Example 1: Location Workflow 224<br/>Situation and Problem Statement 224<br/>Solution Overview 224<br/>Solution Breakdown 226<br/>Example Summary 228<br/>Example 2: Benchmarking Workflow 229<br/>Situation and Problem Statement 229<br/>Solution Overview 229<br/>Solution Breakdown 231<br/>Example Summary 234<br/>Example 3: Proposal Workflow 235<br/>Situation and Problem Statement 235<br/>Solution Overview 236<br/>Solution Breakdown 236<br/>Example Summary 238<br/>Example 4: Improved Forecasting in Agile Projects 238<br/>Situation and Problem Statement 238<br/>Solution Overview 239<br/>Solution Breakdown 240<br/>Example Summary 245<br/>Example 5: Quick Sales Intelligence 246<br/>Situation and Problem Statement 247<br/>Solution Overview 248<br/>Solution Breakdown 249<br/>Example Summary 256<br/>Conclusion 257<br/>Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259<br/>Table of Contents | ix<br/>
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books
Holdings
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    Dewey Decimal Classification     Cummins College of Engineering for Women Pune Cummins College of Engineering for Women Pune 11/10/2024 115 1425.00 3 006.32 KAA CCEP-BK-67496 11/08/2025 26/07/2025 1900.00 11/10/2024 Books