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Artificial Intelligence for Business de Doug Rose

Artificial Intelligence for Business

Autor Doug Rose
en Limba Engleză Paperback – 11 dec 2020

Dacă Machine Learning de Dan Phillips v-a oferit cadrul teoretic și tehnic despre algoritmi, această lucrare, Artificial Intelligence for Business, oferă instrumentele practice necesare liderilor pentru a transforma datele în decizii strategice. Putem afirma că Doug Rose reușește să elimine bariera limbajului matematic avansat pentru a prezenta AI-ul nu ca pe o entitate abstractă, ci ca pe un activ de business gestionabil. Observăm o continuitate logică în opera autorului: dacă în Leading Agile Teams se concentra pe agilitatea organizațională, iar în Data Science explica formarea echipelor interne de analiză, aici Doug Rose închide cercul, oferind managerilor viziunea necesară pentru a integra tehnologiile emergente în fluxul de lucru.

Descoperim aici o structură riguroasă, organizată în cinci părți esențiale. Autorul începe prin a trasa evoluția istorică a mașinilor de gândit, de la conceptele din 1950 până la rețelele neuronale artificiale care mimează creierul uman. Cartea progresează rapid spre aplicații concrete, explicând cum inovațiile recente din GPS și social media alimentează seturile de Big Data necesare pentru Machine Learning. Capitolele dedicate învățării supervizate versus celei nesupervizate și analizei de regresie sunt concepute special pentru a demistifica procesul prin care mașinile „învață” să identifice tipare. Prin exemple practice, cititorul va înțelege cum Internet of Things (IoT) și procesarea limbajului natural pot fi puse la lucru pentru a crește eficiența operațională, transformând frica de necunoscut în oportunitate competitivă.

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Specificații

ISBN-13: 9780136556619
ISBN-10: 0136556612
Pagini: 272
Dimensiuni: 178 x 231 x 18 mm
Greutate: 0.42 kg
Ediția:2nd edition
Editura: Pearson Education

De ce să citești această carte

Această carte se adresează managerilor și profesioniștilor care doresc să înțeleagă potențialul real al AI fără a se pierde în detalii de programare. Veți câștiga capacitatea de a evalua critic ce pot și ce nu pot face tehnologiile ML pentru afacerea dumneavoastră. Este un ghid esențial pentru a trece de la „hype-ul” tehnologic la implementări care aduc valoare adăugată și eficiență în utilizarea Big Data.

Despre autor

Doug Rose este un autor și consultant specializat în managementul resurselor și strategii organizaționale. Cu o experiență vastă în jurnalism și scriere tehnică, el are abilitatea rară de a traduce concepte complexe de inginerie și date în limbaj de business accesibil. Lucrările sale anterioare, axate pe metodologii Agile și formarea echipelor de Data Science, reflectă angajamentul său de a ajuta organizațiile să adopte un mindset modern, orientat spre rezultate concrete și adaptabilitate într-o piață aflată în continuă schimbare tehnologică.

Descriere scurtă

The Easy Introduction to Machine Learning (Ml) for Nontechnical People--In Business and Beyond Artificial Intelligence for Business is your plain-English guide to Artificial Intelligence (AI) and Machine Learning (ML): how they work, what they can and cannot do, and how to start profiting from them. Writing for nontechnical executives and professionals, Doug Rose demystifies AI/ML technology with intuitive analogies and explanations honed through years of teaching and consulting. Rose explains everything from early "expert systems" to advanced deep learning networks.
First, Rose explains how AI and ML emerged, exploring pivotal early ideas that continue to influence the field. Next, he deepens your understanding of key ML concepts, showing how machines can create strategies and learn from mistakes. Then, Rose introduces current powerful neural networks: systems inspired by the structure and function of the human brain. He concludes by introducing leading AI applications, from automated customer interactions to event prediction. Throughout, Rose stays focused on business: applying these technologies to leverage new opportunities and solve real problems.
  • Compare the ways a machine can learn, and explore current leading ML algorithms
  • Start with the right problems, and avoid common AI/ML project mistakes
  • Use neural networks to automate decision-making and identify unexpected patterns
  • Help neural networks learn more quickly and effectively
  • Harness AI chatbots, virtual assistants, virtual agents, and conversational AI applications

Notă biografică

Doug Rose has been transforming organizations through technology, training, and process optimization for more than 25 years. He is the author of the Project Management Institute (PMI) first major publication on the agile framework, Leading Agile Teams. He is also the author of Data Science: Create Teams That Ask the Right Questions and Deliver Real Value and Enterprise Agility for Dummies.


Doug has a master degree (MS) in information management, a law degree (JD) from Syracuse University, and a BA from the University of Wisconsin-Madison. He is also a Scaled Agile Framework Program Consultant (SPC), Certified Technical Trainer (CTT+), Certified Scrum Professional (CSP-SM), Certified Scrum Master (CSM), PMI Agile Certified Professional (PMI-ACP), Project Management Professional (PMP), and Certified Developer for Apache Hadoop (CCDH). You can attend his lively and engaging business and project management courses at the University of Chicago or online through LinkedIn Learning.


Doug works through Doug Enterprises, an organization with an office in whatever city he lives. Currently he lives in Atlanta, Georgia, where he spends his free time either riding a stationary recumbent bike or explaining the Marvel Universe to his son.

Cuprins

Foreword xv Preface xix PART I: Thinking Machines: An Overview of Artificial Intelligence 1 Chapter 1: What Is Artificial Intelligence? 3 What Is Intelligence? 4 Testing Machine Intelligence 6 The General Problem Solver 8 Strong and Weak Artificial Intelligence 11 Artificial Intelligence Planning 14 Learning over Memorizing 15 Chapter Takeaways 18 Chapter 2: The Rise of Machine Learning 19 Practical Applications of Machine Learning 22 Artificial Neural Networks 24 The Fall and Rise of the Perceptron 27 Big Data Arrives 30 Chapter Takeaways 33 Chapter 3: Zeroing in on the Best Approach 35 Expert System Versus Machine Learning 35 Supervised Versus Unsupervised Learning 37 Backpropagation of Errors 38 Regression Analysis 41 Chapter Takeaways 43 Chapter 4: Common AI Applications 45 Intelligent Robots 45 Natural Language Processing 48 The Internet of Things 50 Chapter Takeaways 51 Chapter 5: Putting AI to Work on Big Data 53 Understanding the Concept of Big Data 54 Teaming Up with a Data Scientist 54 Machine Learning and Data Mining: Whats the Difference? 55 Making the Leap from Data Mining to Machine Learning 56 Taking the Right Approach 57 Chapter Takeaways 59 Chapter 6: Weighing Your Options 61 Chapter Takeaways 64 PART II: Machine Learning 65 Chapter 7: What Is Machine Learning? 67 How a Machine Learns 71 Working with Data 74 Applying Machine Learning 77 Different Types of Learning 79 Chapter Takeaways 81 Chapter 8: Different Ways a Machine Learns 83 Supervised Machine Learning 83 Unsupervised Machine Learning 86 Semi-Supervised Machine Learning 89 Reinforcement Learning 91 Chapter Takeaways 93 Chapter 9: Popular Machine Learning Algorithms 95 Decision Trees 99 k-Nearest Neighbor 101 k-Means Clustering 104 Regression Analysis 108 Naive Bayes 110 Chapter Takeaways 113 Chapter 10: Applying Machine Learning Algorithms 115 Fitting the Model to Your Data 119 Choosing Algorithms 120 Ensemble Modeling 121 Deciding on a Machine Learning Approach 123 Chapter Takeaways 124 Chapter 11: Words of Advice 125 Start Asking Questions 125 Dont Mix Training Data with Test Data 127 Dont Overstate a Models Accuracy 127 Know Your Algorithms 128 Chapter Takeaways 128 PART III: Artificial Neural Networks 129 Chapter 12: What Are Artificial Neural Networks? 131 Why the Brain Analogy? 133 Just Another Amazing Algorithm 133 Getting to Know the Perceptron 135 Squeezing Down a Sigmoid Neuron 138 Adding Bias 141 Chapter Takeaways 142 Chapter 13: Artificial Neural Networks in Action 143 Feeding Data into the Network 143 What Goes on in the Hidden Layers 145 Understanding Activation Functions 149 Adding Weights 151 Adding Bias 152 Chapter Takeaways 153 Chapter 14: Letting Your Network Learn 155 Starting with Random Weights and Biases 156 Making Your Network Pay for Its Mistakes: The Cost Function 157 Combining the Cost Function with Gradient Descent 158 Using Backpropagation to Correct for Errors 160 Tuning Your Network 163 Employing the Chain Rule 164 Batching the Data Set with Stochastic Gradient Descent 166 Chapter Takeaways 167 Chapter 15: Using Neural Networks to Classify or Cluster 169 Solving Classification Problems 170 Solving Clustering Problems 172 Chapter Takeaways 174 Chapter 16: Key Challenges 175 Obtaining Enough Quality Data 175 Keeping Training and Test Data Separate 176 Carefully Choosing Your Training Data 177 Taking an Exploratory Approach 177 Choosing the Right Tool for the Job 178 Chapter Takeaways 178 PART IV: Putting Artificial Intelligence to Work 179 Chapter 17: Harnessing the Power of Natural Language Processing 181 Extracting Meaning from Text and Speech with NLU 183 Delivering Sensible Responses with NLG 184 Automating Customer Service 186 Reviewing the Top NLP Tools and Resources 187 NLU Tools 189 NLG Tools 190 Chapter Takeaways 191 Chapter 18: Automating Customer Interactions 193 Choosing Natural Language Technologies 195 Review the Top Tools for Creating Chatbots and Virtual Agents 196 Chapter Takeaways 198 Chapter 19: Improving Data-Based Decision-Making 199 Choosing Between Automated and Intuitive Decision-Making 201 Gathering Data in Real Time from IoT Devices 202 Reviewing Automated Decision-Making Tools 204 Chapter Takeaways 205 Chapter 20: Using Machine Learning to Predict Events and Outcomes 207 Machine Learning Is Really about Labeling Data 208 Looking at What Machine Learning Can Do 210 Predict What Customers Will Buy 210 Answer Questions Before Theyre Asked 210 Make Better Decisions Faster 212 Replicate Expertise in Your Business 213 Use Your Power for Good, Not Evil: Machine Learning Ethics 214 Review the Top Machine Learning Tools 216 Chapter Takeaways 218 Chapter 21: Building Artificial Minds 219 Separating Intelligence from Automation 221 Adding Layers for Deep Learning 222 Considering Applications for Artificial Neural Networks 223 Classifying Your Best Customers 224 Recommending Store Layouts 225 Analyzing and Tracking Biometrics 226 Reviewing the Top Deep Learning Tools 228 Chapter Takeaways 229 Index 231