Tabla de Contenidos: “…; Do 2: Making and Taking the Instant Interview Magic Potion; Do 3: Maintaining Magic Potion Potency Throughout the Day; Do 4: Breaking Out of the Box-Job E-X-P-A-N-S-I-O-N; Do 5: E-Mailing, Faxing, and Mailing Your Rest-You-Mary; Do 6: Getting Your Resume Scanned; Do 7: Writing the ASCII Resume; Do 8: Getting You Covered with the Better Letter; Do 9: Broadcasting a Letter with High Bandwidth…”

Sumario

“…When you focus on the three things that you must get right every day, you begin to be the best at what matters most, and that's what gets more customers, increases sales, increases revenue, and grows the business"--…”

“…MAKE YOUR LEADERSHIP EFFORTS STICK Every day, thousands of people put great effort--and money--into becoming more effective leaders, through seminars, personal coaching, and employee development plans. …”

“…This book provides deep understanding of RAC concepts and implementation details that you can apply toward your day-to-day operational practices. You’ll be guided in troubleshooting and avoiding trouble in your installation. …”

Tabla de Contenidos: “…Part 3 Playing with routers -- 10 Exact-match lookups -- 10.1 Challenge 1: Ethernet under fire -- 10.2 Challenge 2: wire speed forwarding -- 10.3 Challenge 3: scaling lookups to higher speeds -- 10.3.1 Scaling via hashing -- 10.3.2 Using hardware parallelism -- 10.3.3 The d-left approach -- 10.4 Summary -- 10.5 Exercise -- 11 Prefix-match lookups -- 11.1 Introduction to prefix lookups -- 11.1.1 Prefix notation -- 11.1.2 Why variable-length prefixes? …”

Tabla de Contenidos: “…. -- Chapter 4 The Discovery of They Ask, You Answer -- Chapter 5 "They Ask, You Answer" Defined -- Chapter 6 Brainstorming the Questions You Are Asked Every Day -- Putting It Into Action -- Chapter 7 The Ostrich Marketing Strategy -- Chapter 8 The CarMax Effect -- The Beginnings of CarMax and a New Way to Sell -- Putting It Into Action -- Chapter 9 The Discovery of the Big 5 -- Chapter 10 Content Subject 1 Pricing and Costs: Why We Must Talk about Money -- 1. …”

Tabla de Contenidos: “…-- Quick Start Action: Questions to Ask a Consultant -- Quick Start Lists -- 2 Planning Your Consulting Future -- Chapter Overview -- Your Preferred Future -- Describe Your Ideal Day -- Describe Your Future -- Will Consulting Lead You to Your Life Goals? …”

“…This book presents a nine-step approach for configuring a GPFS cluster, and also presents various tools and techniques that make it easier for a systems administrator in GPFS implementation and day-to-day management tasks…”

Tabla de Contenidos: “…8.5 Cost and Return on Investment -- References -- Part III A Perfect Match -- Chapter 9 Habits and Learning -- 9.1 New Habits Tools -- 9.2 How the World of Training Is Changing -- 9.2.1 Communication -- 9.3 E-learning -- 9.3.1 Usefulness -- 9.3.2 Why Companies Use It -- 9.3.3 Challenges -- 9.3.4 On Working Days -- References -- Chapter 10 Training and Coaching 3.0 -- 10.1 What Does Training Mean? …”

Tabla de Contenidos: “…-- 4.2.5 Packet Scheduling -- 4.3 The Internet Protocol (IP): IPv4, Addressing, IPv6, and More -- 4.3.1 IPv4 Datagram Format -- 4.3.2 IPv4 Addressing -- 4.3.3 Network Address Translation (NAT) -- 4.3.4 IPv6 -- 4.4 Generalized Forwarding and SDN -- 4.4.1 Match -- 4.4.2 Action -- 4.4.3 OpenFlow Examples of Match-plus-action in Action -- 4.5 Middleboxes -- 4.6 Summary -- Homework Problems and Questions -- Wireshark Lab: IP -- Interview: Vinton G. …”

Tabla de Contenidos: “…-- Respecting Demographic Data -- Notes -- Chapter 5 Analyze the Data -- Seeing It So Clear Now -- Using the Data to Know What You Have -- The How: Analyzing Data to Identify Recruitment Issues -- The How: Analyzing Data to Identify Retention Issues -- Assume Competence -- Notes -- Chapter 6 Plan your Program -- The World's Park -- Plan Only after You Analyze the Data -- Getting Started on Your Plan -- 4-2-50 Program -- How to Measure ROI in Your DE&amp -- I Program -- The How: CAPE DE&amp -- I Plan Template -- How to Complete the CAPE DE&amp -- I Template -- Note -- Chapter 7 Execute on the Plan -- Audio Must Match Video -- E Is for Execute -- The How: A CAPE Path to Execute -- Part II Practical Lessons Learned in DE&amp -- I Programs -- Chapter 8 DE&amp -- I Programs Should Look Like the Company…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Foreword -- Contributors -- Table of Contents -- Preface -- Part 1: What Data-Centric Machine Learning Is and Why We Need It -- Chapter 1: Exploring Data-Centric Machine Learning -- Understanding data-centric ML -- The origins of data centricity -- The components of ML systems -- Data is the foundational ingredient -- Data-centric versus model-centric ML -- Data centricity is a team sport -- The importance of quality data in ML -- Identifying high-value legal cases with natural language processing -- Predicting cardiac arrests in emergency calls -- Summary -- References -- Chapter 2: From Model-Centric to Data-Centric - ML's Evolution -- Exploring why ML development ended up being mostly model-centric -- The 1940s to 1970s - the early days -- The 1980s to 1990s - the rise of personal computing and the internet -- The 2000s - the rise of tech giants -- 2010-now - big data drives AI innovation -- Model-centricity was the logical evolutionary outcome -- Unlocking the opportunity for small data ML -- Why we need data-centric AI more than ever -- The cascading effects of data quality -- Avoiding data cascades and technical debt -- Summary -- References -- Part 2: The Building Blocks of Data-Centric ML -- Chapter 3: Principles of Data-Centric ML -- Sometimes, all you need is the right data -- Principle 1 - data should be the center of ML development -- A checklist for data-centricity -- Principle 2 - leverage annotators and SMEs effectively -- Direct labeling with human annotators -- Verifying output quality with human annotators -- Codifying labeling rules with programmatic labeling -- Principle 3 - use ML to improve your data -- Principle 4 - follow ethical, responsible, and well-governed ML practices -- Summary -- References -- Chapter 4: Data Labeling Is a Collaborative Process…”