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Achieving Digital Transformation Using Hybrid Cloud : Design Standardized Next-Generation Applications for Any Infrastructure
| Achieving Digital Transformation Using Hybrid Cloud : Design Standardized Next-Generation Applications for Any Infrastructure |
| Autore | Grover Vikas |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Birmingham : , : Packt Publishing, Limited, , 2023 |
| Descrizione fisica | 1 online resource (234 pages) |
| Disciplina | 004.67/82 |
| Altri autori (Persone) |
VermaIshu
RajagopalanPraveen |
| Soggetto topico |
Cloud computing
Storage area networks (Computer networks) |
| ISBN |
9781837634156
1837634157 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright and Credits -- Contributors -- About the reviewers -- Table of Contents -- Preface -- Part 1: Containers, Kubernetes, and DevOps for Hybrid Cloud -- Chapter 1: Adopting the Right Strategy for Building a Hybrid Cloud -- Exploring cloud computing -- types and service delivery models -- Defining the hybrid cloud -- Variations in the hybrid cloud -- homogeneous and heterogeneous -- Hybrid cloud use cases -- Understanding the benefits of hybrid cloud computing -- Hybrid cloud strategies -- Addressing compliance considerations -- Automating security measures
Finding the right balance between public and private clouds -- Evaluating available tools and technologies -- Summary -- Further reading -- Chapter 2: Dealing with VMs, Containers, and Kubernetes -- Introduction to VM and containers -- VMs -- Containers -- Anatomy of containers -- About OCI and Docker -- The differences between VMs and containers -- Container orchestration -- Why do we need container orchestration? -- Kubernetes -- a container orchestration tool -- OpenShift -- AWS EKS -- Azure Kubernetes Service (AKS) -- VMware Tanzu Kubernetes Grid (TKG) -- HashiCorp Nomad Google Kubernetes Engine (GKE) -- Docker Swarm -- CI/CD on the hybrid cloud -- Summary -- Further reading -- Chapter 3: Provisioning Infrastructure with IaC -- Infrastructure provisioning overview -- Virtualizing hardware with SDI -- Provisioning IaaS -- Provisioning and managing infrastructure with IaC -- Imperative and declarative frameworks -- Imperative and declarative framework tools for IaC -- Considerations for IaC -- Accelerating IT service delivery with DevOps -- CI/CD -- Continuous testing -- Continuous operations -- Monitoring and observability Automating delivery and deployment with GitOps -- Push versus pull deployments -- Enabling GitOps using Argo CD -- Best practices for GitOps -- Summary -- Further reading -- Chapter 4: Communicating across Kubernetes -- Pod design patterns -- The sidecar pattern -- The adapter pattern -- The ambassador pattern -- Container-to-container communication -- Pod-to-pod communication -- Pods with multiple interfaces -- Pod-to-service communication -- External-to-service communication -- How to discover pods and services -- How to publish services -- How to stitch multiple K8s clusters Submariner -- using layer 3 networking -- Skupper -- using a common application network (layer 7) -- Service meshes -- Federation of service meshes -- Summary -- Further reading -- Part 2: Design Patterns, DevOps, and GitOps -- Chapter 5: Design Patterns for Telcos and Industrial Sectors -- Applying design patterns for operational excellence -- Telco -- Creating your own pattern -- Defining a framework -- Cloud-friendly -- A common application platform -- Consistent management -- Automation -- Summary -- Further reading -- Chapter 6: Securing the Hybrid Cloud |
| Record Nr. | UNINA-9911007151903321 |
Grover Vikas
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| Birmingham : , : Packt Publishing, Limited, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advanced deep learning with Keras : apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more / / Rowel Atienza
| Advanced deep learning with Keras : apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more / / Rowel Atienza |
| Autore | Atienza Rowel |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | London, England : , : Packt Publishing, Limited, , [2018] |
| Descrizione fisica | 1 online resource (368 pages) |
| Disciplina | 006.32 |
| Soggetto topico |
Machine learning
Neural networks (Computer science) |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Copyright -- Packt upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Introducing Advanced Deep Learning with Keras -- Why is Keras the perfect deep learning library? -- Installing Keras and TensorFlow -- Implementing the core deep learning models - MLPs, CNNs and RNNs -- The difference between MLPs, CNNs, and RNNs -- Multilayer perceptrons (MLPs) -- MNIST dataset -- MNIST digits classifier model -- Building a model using MLPs and Keras -- Regularization -- Output activation and loss function -- Optimization -- Performance evaluation -- Model summary -- Convolutional neural networks (CNNs) -- Convolution -- Pooling operations -- Performance evaluation and model summary -- Recurrent neural networks (RNNs) -- Conclusion -- Chapter 2: Deep Neural Networks -- Functional API -- Creating a two-input and one-output model -- Deep residual networks (ResNet) -- ResNet v2 -- Densely connected convolutional networks (DenseNet) -- Building a 100-layer DenseNet-BC for CIFAR10 -- Conclusion -- References -- Chapter 3: Autoencoders -- Principles of autoencoders -- Building autoencoders using Keras -- Denoising autoencoder (DAE) -- Automatic colorization autoencoder -- Conclusion -- References -- Chapter 4: Generative Adversarial Networks (GANs) -- An overview of GANs -- Principles of GANs -- GAN implementation in Keras -- Conditional GAN -- Conclusion -- References -- Chapter 5: Improved GANs -- Wasserstein GAN -- Distance functions -- Distance function in GANs -- Use of Wasserstein loss -- WGAN implementation using Keras -- Least-squares GAN (LSGAN) -- Auxiliary classifier GAN (ACGAN) -- Conclusion -- References -- Chapter 6: Disentangled Representation GANs -- Disentangled representations -- InfoGAN -- Implementation of InfoGAN in Keras -- Generator outputs of InfoGAN -- StackedGAN -- Implementation of StackedGAN in Keras.
Generator outputs of StackedGAN -- Conclusion -- Reference -- Chapter 7: Cross-Domain GANs -- Principles of CycleGAN -- The CycleGAN Model -- Implementing CycleGAN using Keras -- Generator outputs of CycleGAN -- CycleGAN on MNIST and SVHN datasets -- Conclusion -- References -- Chapter 8: Variational Autoencoders (VAEs) -- Principles of VAEs -- Variational inference -- Core equation -- Optimization -- Reparameterization trick -- Decoder testing -- VAEs in Keras -- Using CNNs for VAEs -- Conditional VAE (CVAE) -- -VAE: VAE with disentangled latent representations -- Conclusion -- References -- Chapter 9: Deep Reinforcement Learning -- Principles of reinforcement learning (RL) -- The Q value -- Q-Learning example -- Q-Learning in Python -- Nondeterministic environment -- Temporal-difference learning -- Q-Learning on OpenAI gym -- Deep Q-Network (DQN) -- DQN on Keras -- Double Q-Learning (DDQN) -- Conclusion -- References -- Chapter 10: Policy Gradient Methods -- Policy gradient theorem -- Monte Carlo policy gradient (REINFORCE) method -- REINFORCE with baseline method -- Actor-Critic method -- Advantage Actor-Critic (A2C) method -- Policy Gradient methods with Keras -- Performance evaluation of policy gradient methods -- Conclusion -- References -- Other Books You May Enjoy -- Index. |
| Record Nr. | UNINA-9910795323903321 |
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Atienza Rowel
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| London, England : , : Packt Publishing, Limited, , [2018] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advanced deep learning with Keras : apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more / / Rowel Atienza
| Advanced deep learning with Keras : apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more / / Rowel Atienza |
| Autore | Atienza Rowel |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | London, England : , : Packt Publishing, Limited, , [2018] |
| Descrizione fisica | 1 online resource (368 pages) |
| Disciplina | 006.32 |
| Soggetto topico |
Machine learning
Neural networks (Computer science) |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Copyright -- Packt upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Introducing Advanced Deep Learning with Keras -- Why is Keras the perfect deep learning library? -- Installing Keras and TensorFlow -- Implementing the core deep learning models - MLPs, CNNs and RNNs -- The difference between MLPs, CNNs, and RNNs -- Multilayer perceptrons (MLPs) -- MNIST dataset -- MNIST digits classifier model -- Building a model using MLPs and Keras -- Regularization -- Output activation and loss function -- Optimization -- Performance evaluation -- Model summary -- Convolutional neural networks (CNNs) -- Convolution -- Pooling operations -- Performance evaluation and model summary -- Recurrent neural networks (RNNs) -- Conclusion -- Chapter 2: Deep Neural Networks -- Functional API -- Creating a two-input and one-output model -- Deep residual networks (ResNet) -- ResNet v2 -- Densely connected convolutional networks (DenseNet) -- Building a 100-layer DenseNet-BC for CIFAR10 -- Conclusion -- References -- Chapter 3: Autoencoders -- Principles of autoencoders -- Building autoencoders using Keras -- Denoising autoencoder (DAE) -- Automatic colorization autoencoder -- Conclusion -- References -- Chapter 4: Generative Adversarial Networks (GANs) -- An overview of GANs -- Principles of GANs -- GAN implementation in Keras -- Conditional GAN -- Conclusion -- References -- Chapter 5: Improved GANs -- Wasserstein GAN -- Distance functions -- Distance function in GANs -- Use of Wasserstein loss -- WGAN implementation using Keras -- Least-squares GAN (LSGAN) -- Auxiliary classifier GAN (ACGAN) -- Conclusion -- References -- Chapter 6: Disentangled Representation GANs -- Disentangled representations -- InfoGAN -- Implementation of InfoGAN in Keras -- Generator outputs of InfoGAN -- StackedGAN -- Implementation of StackedGAN in Keras.
Generator outputs of StackedGAN -- Conclusion -- Reference -- Chapter 7: Cross-Domain GANs -- Principles of CycleGAN -- The CycleGAN Model -- Implementing CycleGAN using Keras -- Generator outputs of CycleGAN -- CycleGAN on MNIST and SVHN datasets -- Conclusion -- References -- Chapter 8: Variational Autoencoders (VAEs) -- Principles of VAEs -- Variational inference -- Core equation -- Optimization -- Reparameterization trick -- Decoder testing -- VAEs in Keras -- Using CNNs for VAEs -- Conditional VAE (CVAE) -- -VAE: VAE with disentangled latent representations -- Conclusion -- References -- Chapter 9: Deep Reinforcement Learning -- Principles of reinforcement learning (RL) -- The Q value -- Q-Learning example -- Q-Learning in Python -- Nondeterministic environment -- Temporal-difference learning -- Q-Learning on OpenAI gym -- Deep Q-Network (DQN) -- DQN on Keras -- Double Q-Learning (DDQN) -- Conclusion -- References -- Chapter 10: Policy Gradient Methods -- Policy gradient theorem -- Monte Carlo policy gradient (REINFORCE) method -- REINFORCE with baseline method -- Actor-Critic method -- Advantage Actor-Critic (A2C) method -- Policy Gradient methods with Keras -- Performance evaluation of policy gradient methods -- Conclusion -- References -- Other Books You May Enjoy -- Index. |
| Record Nr. | UNINA-9910819310903321 |
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Atienza Rowel
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| London, England : , : Packt Publishing, Limited, , [2018] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advanced Python Programming : accelerate your python programs using proven techniques and design patterns / / Quan Nguyen
| Advanced Python Programming : accelerate your python programs using proven techniques and design patterns / / Quan Nguyen |
| Autore | Nguyễn Quân |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Birmingham, United Kingdom : , : Packt Publishing, Limited, , [2022] |
| Descrizione fisica | 1 online resource (xxiv, 576 pages) : illustrations |
| Disciplina | 005.72 |
| Soggetto topico |
Python (Computer program language)
Application software - Development |
| ISBN |
9781523151370
1523151374 9781801817776 1801817774 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Table of Contents Benchmarking and Profiling Pure Python Optimizations Fast Array Operations with NumPy and Pandas C Performance with Cython Exploring Compilers Automatic Differentiation and Accelerated Linear Algebra for Machine Learning Implementing Concurrency Parallel Processing Concurrent Web Requests Concurrent Image Processing Building Communication Channels with asyncio Deadlocks Starvation Race Conditions The Global Interpreter Lock The Factory Pattern The Builder Pattern Other Creational Patterns The Adapter Pattern The Decorator Pattern The Bridge Pattern The Façade Pattern Other Structural Patterns The Chain of Responsibility Pattern The Command Pattern The Observer Pattern. |
| Record Nr. | UNINA-9911007172103321 |
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Nguyễn Quân
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| Birmingham, United Kingdom : , : Packt Publishing, Limited, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Agile Model-Based Systems Engineering Cookbook : Improve System Development by Applying Proven Recipes for Effective Agile Systems Engineering
| Agile Model-Based Systems Engineering Cookbook : Improve System Development by Applying Proven Recipes for Effective Agile Systems Engineering |
| Autore | Douglass Bruce Powel |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Birmingham : , : Packt Publishing, Limited, , 2021 |
| Descrizione fisica | 1 online resource (601 pages) |
| Disciplina | 620/.001171 |
| Altri autori (Persone) | HolstChristian von |
| Collana | Expert insight |
| Soggetto topico |
Systems engineering
Agile software development |
| ISBN |
9781523151424
1523151420 9781803234304 180323430X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Contributors -- Table of contents -- Preface -- Chapter 1: Basics of Agile Systems Modeling -- What's agile all about? -- Model-Based Systems Engineering (MBSE) -- Managing your backlog -- Purpose -- Inputs and preconditions -- Outputs and postconditions -- How to do it -- Example -- Measuring your success -- How to do it -- Example -- Some considerations -- Managing risk -- Purpose -- Inputs and proconditions -- Outputs and postconditions -- How to do it -- Example -- Product roadmap -- Purpose -- Inputs and preconditions -- How to do it -- Example -- Release plan -- Purpose -- Inputs and preconditons -- Outputs and postconditions -- How to do it -- Example -- Iteration plan -- Purpose -- Inputs and preconditions -- Outputs and postconditions -- How to do it -- Example -- Estimating Effort -- Purpose -- Inputs and preconditions -- Outputs and postconditions -- How to do it -- How it works -- Example -- Work item prioritization -- Purpose -- Inputs and preconditions -- How to do it -- How it works -- Example -- Iteration 0 -- Purpose -- Inputs and preconditions -- Outputs and postconditions -- How to do it -- Example -- Architecture 0 -- Subsystem and component view -- Concurrency and resource view -- Distribution view -- Dependability view -- Deployment view -- Purpose -- Inputs and preconditions -- Outputs and postconditions -- How to do it -- Example -- Additional note -- Organizing your models -- Purpose -- Inputs and preconditions -- Outputs and postconditions -- How to do it -- How it works -- Example -- Managing change -- Purpose -- Inputs and preconditions -- How to do it -- Example -- Chapter 2: System Specification -- Recipes in this chapter -- Why aren't textual requirements enough? -- Definitions -- Functional Analysis with Scenarios -- Purpose -- Inputs and preconditions -- Outputs and postconditions -- How to do it.
Example -- Functional analysis with activities -- Inputs and preconditions -- Outputs and postconditions -- How to do it -- Functional analysis with state machines -- Purpose -- Inputs and preconditions -- Outputs and postconditions -- How to do it -- Example -- Functional Analysis with User Stories -- A little bit about user stories -- Purpose -- Inputs and preconditions -- Outputs and postconditions -- How to do it -- Example -- Model-Based Safety Analysis -- A little bit about safety analysis -- Some Profiles -- Hazard analysis -- Purpose -- Inputs and preconditions -- Outputs and postconditions -- How to do it -- Example -- Model-Based Threat Analysis -- Basics of Cyber-Physical Security -- Modeling for Security Analysis -- Purpose -- Inputs and preconditions -- Outputs and postconditions -- How to do it -- Example -- Specifying Logical System Interfaces -- A Note about SysML Ports and Interfaces -- Purpose -- Inputs and preconditions -- Outputs and postconditions -- How to do it -- Example -- Creating the Logical Data Schema -- Definitions -- Example -- Purpose -- Inputs and preconditions -- Outputs and postconditions -- How to do it -- Example -- Chapter 3: Developing System Architectures -- Recipes in this chapter -- Five critical views of architecture -- General architectural guidelines -- Architectural trade studies -- Purpose -- Inputs and preconditions -- Outputs and postconditions -- How to do it -- Example -- Architectural merge -- Example -- Pattern-driven architecture -- Purpose -- Inputs and preconditions -- Outputs and postconditions -- How to do it -- Example -- Subsystem and component architecture -- Purpose -- Inputs and preconditions -- Outputs and postconditions -- How to do it -- Example -- Architectural allocation -- Creating subsystem interfaces from use case scenarios -- Purpose -- Inputs and preconditions. Outputs and postconditions -- How to do it -- Specializing a reference architecture -- Purpose -- Inputs and preconditions -- Outputs and postconditions -- How to do it -- Chapter 4: Handoff to Downstream Engineering -- Recipes in this chapter -- Activities for the handoff to downstream engineering -- Starting point for the examples -- Preparation for Handoff -- Federating Models for Handoff -- Logical to Physical Interfaces -- Deployment Architecture I: Allocation to Engineering Facets -- Deployment Architecture II: Interdisciplinary Interfaces -- Chapter 5: Demonstration of Meeting Needs: Verification and Validation -- Recipes in this chapter -- Verification and validation -- Model simulation -- Purpose -- Inputs and preconditions -- Outputs and postconditions -- How to do it -- Example -- Model-based testing -- Inputs and preconditions -- Outputs and postconditions -- How to do it -- Example -- Computable constraint modeling -- Purpose -- Inputs and preconditions -- How to do it -- Example -- Traceability -- Purpose -- Inputs and preconditions -- Outputs and postconditions -- How to do it -- Example -- Effective Reviews and walkthroughs -- Purpose -- Inputs and preconditions -- Outputs and postconditions -- How to do it -- Example -- Managing Model Work Items -- Purpose -- Inputs and preconditions -- How to do it -- Example -- Test Driven Modeling -- Purpose -- Inputs and preconditions -- Outputs and postconditions -- How to do it -- Example -- Appendix A: The Pegasus Bike Trainer -- Overview -- Pegasus High-Level Features -- Highly customizable bike fit -- Monitor exercise metrics -- Export/upload exercise metrics -- Variable power output -- Gearing emulation -- Controllable power level -- Incline control -- User interface -- Online training system compatible -- Configuration and OTA firmware updates -- Packt page -- Other Books You May Enjoy. Index. |
| Record Nr. | UNINA-9911006678303321 |
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Douglass Bruce Powel
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| Birmingham : , : Packt Publishing, Limited, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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AI Agents in Practice : Design, Implement, and Scale Autonomous AI Systems for Production
| AI Agents in Practice : Design, Implement, and Scale Autonomous AI Systems for Production |
| Autore | Alto Valentina |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Birmingham : , : Packt Publishing, Limited, , 2025 |
| Descrizione fisica | 1 online resource (282 pages) |
| Disciplina | 006.3 |
| Soggetto topico | Intelligent agents (Computer software) |
| ISBN | 9781805801344 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9911044024703321 |
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Alto Valentina
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| Birmingham : , : Packt Publishing, Limited, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Apache Hadoop 3 quick start guide : learn about big data processing and analytics / / Hrishikesh Vijay Karambelkar
| Apache Hadoop 3 quick start guide : learn about big data processing and analytics / / Hrishikesh Vijay Karambelkar |
| Autore | Karambelkar Hrishikesh Vijay |
| Edizione | [First edition] |
| Pubbl/distr/stampa | London, England : , : Packt Publishing, Limited, , [2018] |
| Descrizione fisica | 1 online resource (220 pages) |
| Disciplina | 004.36 |
| Soggetto topico |
Cloud computing
Electronic data processing - Distributed processing - Management |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910795325303321 |
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Karambelkar Hrishikesh Vijay
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| London, England : , : Packt Publishing, Limited, , [2018] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Apache Hadoop 3 quick start guide : learn about big data processing and analytics / / Hrishikesh Vijay Karambelkar
| Apache Hadoop 3 quick start guide : learn about big data processing and analytics / / Hrishikesh Vijay Karambelkar |
| Autore | Karambelkar Hrishikesh Vijay |
| Edizione | [First edition] |
| Pubbl/distr/stampa | London, England : , : Packt Publishing, Limited, , [2018] |
| Descrizione fisica | 1 online resource (220 pages) |
| Disciplina | 004.36 |
| Soggetto topico |
Cloud computing
Electronic data processing - Distributed processing - Management |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910814241203321 |
|
Karambelkar Hrishikesh Vijay
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| London, England : , : Packt Publishing, Limited, , [2018] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Applied Machine Learning for Healthcare and Life Sciences Using AWS : Transformational AI Implementations for Biotech, Clinical, and Healthcare Organizations
| Applied Machine Learning for Healthcare and Life Sciences Using AWS : Transformational AI Implementations for Biotech, Clinical, and Healthcare Organizations |
| Autore | Ratan Ujjwal |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Birmingham : , : Packt Publishing, Limited, , 2022 |
| Descrizione fisica | 1 online resource (224 pages) |
| Disciplina | 006.3/1 |
| Soggetto topico |
Machine learning
Artificial intelligence - Medical applications Cloud computing Web services Medical care - Computer simulation Life sciences - Computer simulation |
| ISBN |
9781523151530
1523151536 9781804619193 1804619191 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Table of Contents Introducing Machine Learning and the AWS Machine Learning Stack Exploring Key AWS Machine Learning Services for Healthcare and Life Sciences Machine Learning for Patient Risk Stratification Using Machine Learning to Improve Operational Efficiency for Healthcare Providers Implementing Machine Learning for Healthcare Payors Implementing Machine Learning for Medical Devices and Radiology Images Applying Machine Learning to Genomics Applying Machine Learning to Molecular Data Applying Machine Learning to Clinical Trials and Pharmacovigilance Utilizing Machine Learning in the Pharmaceutical Supply Chain Understanding Common Industry Challenges and Solutions Understanding Current Industry Trends and Future Applications. |
| Record Nr. | UNINA-9911004743403321 |
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Ratan Ujjwal
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| Birmingham : , : Packt Publishing, Limited, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Artificial Intelligence by Example : Acquire Advanced AI, Machine Learning, and Deep Learning Design Skills
| Artificial Intelligence by Example : Acquire Advanced AI, Machine Learning, and Deep Learning Design Skills |
| Autore | Rothman Denis |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Birmingham : , : Packt Publishing, Limited, , 2020 |
| Descrizione fisica | 1 online resource (579 pages) |
| Disciplina | 6.3 |
| Soggetto topico | Artificial intelligence |
| ISBN | 1-83921-281-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Copyright -- Packt Page -- Contributors -- Table of Contents -- Preface -- Chapter 1: Getting Started with Next-Generation Artificial Intelligence through Reinforcement Learning -- Reinforcement learning concepts -- How to adapt to machine thinking and become an adaptive thinker -- Overcoming real-life issues using the three-step approach -- Step 1 - describing a problem to solve: MDP in natural language -- Watching the MDP agent at work -- Step 2 - building a mathematical model: the mathematical representation of the Bellman equation and MDP -- From MDP to the Bellman equation -- Step 3 - writing source code: implementing the solution in Python -- The lessons of reinforcement learning -- How to use the outputs -- Possible use cases -- Machine learning versus traditional applications -- Summary -- Questions -- Further reading -- Chapter 2: Building a Reward Matrix - Designing Your Datasets -- Designing datasets - where the dream stops and the hard work begins -- Designing datasets -- Using the McCulloch-Pitts neuron -- The McCulloch-Pitts neuron -- The Python-TensorFlow architecture -- Logistic activation functions and classifiers -- Overall architecture -- Logistic classifier -- Logistic function -- Softmax -- Summary -- Questions -- Further reading -- Chapter 3: Machine Intelligence - Evaluation Functions and Numerical Convergence -- Tracking down what to measure and deciding how to measure it -- Convergence -- Implicit convergence -- Numerically controlled gradient descent convergence -- Evaluating beyond human analytic capacity -- Using supervised learning to evaluate a result that surpasses human analytic capacity -- Summary -- Questions -- Further reading -- Chapter 4: Optimizing Your Solutions with K-Means Clustering -- Dataset optimization and control -- Designing a dataset and choosing an ML/DL model.
Approval of the design matrix -- Implementing a k-means clustering solution -- The vision -- The data -- The strategy -- The k-means clustering program -- The mathematical definition of k-means clustering -- The Python program -- Saving and loading the model -- Analyzing the results -- Bot virtual clusters as a solution -- The limits of the implementation of the k-means clustering algorithm -- Summary -- Questions -- Further reading -- Chapter 5: How to Use Decision Trees to Enhance K-Means Clustering -- Unsupervised learning with KMC with large datasets -- Identifying the difficulty of the problem -- NP-hard - the meaning of P -- NP-hard - the meaning of non-deterministic -- Implementing random sampling with mini-batches -- Using the LLN -- The CLT -- Using a Monte Carlo estimator -- Trying to train the full training dataset -- Training a random sample of the training dataset -- Shuffling as another way to perform random sampling -- Chaining supervised learning to verify unsupervised learning -- Preprocessing raw data -- A pipeline of scripts and ML algorithms -- Step 1 - training and exporting data from an unsupervised ML algorithm -- Step 2 - training a decision tree -- Step 3 - a continuous cycle of KMC chained to a decision tree -- Random forests as an alternative to decision trees -- Summary -- Questions -- Further reading -- Chapter 6: Innovating AI with Google Translate -- Understanding innovation and disruption in AI -- Is AI disruptive? -- AI is based on mathematical theories that are not new -- Neural networks are not new -- Looking at disruption - the factors that are making AI disruptive -- Cloud server power, data volumes, and web sharing of the early 21st century -- Public awareness -- Inventions versus innovations -- Revolutionary versus disruptive solutions -- Where to start? -- Discover a world of opportunities with Google Translate. Getting started -- The program -- The header -- Implementing Google's translation service -- Google Translate from a linguist's perspective -- Playing with the tool -- Linguistic assessment of Google Translate -- AI as a new frontier -- Lexical field and polysemy -- Exploring the frontier - customizing Google Translate with a Python program -- k-nearest neighbor algorithm -- Implementing the KNN algorithm -- The knn_polysemy.py program -- Implementing the KNN function in Google_Translate_Customized.py -- Conclusions on the Google Translate customized experiment -- The disruptive revolutionary loop -- Summary -- Questions -- Further reading -- Chapter 7: Optimizing Blockchains with Naive Bayes -- Part I - the background to blockchain technology -- Mining bitcoins -- Using cryptocurrency -- PART II - using blockchains to share information in a supply chain -- Using blockchains in the supply chain network -- Creating a block -- Exploring the blocks -- Part III - optimizing a supply chain with naive Bayes in a blockchain process -- A naive Bayes example -- The blockchain anticipation novelty -- The goal - optimizing storage levels using blockchain data -- Implementation of naive Bayes in Python -- Gaussian naive Bayes -- Summary -- Questions -- Further reading -- Chapter 8: Solving the XOR Problem with a Feedforward Neural Network -- The original perceptron could not solve the XOR function -- XOR and linearly separable models -- Linearly separable models -- The XOR limit of a linear model, such as the original perceptron -- Building an FNN from scratch -- Step 1 - defining an FNN -- Step 2 - an example of how two children can solve the XOR problem every day -- Implementing a vintage XOR solution in Python with an FNN and backpropagation -- A simplified version of a cost function and gradient descent -- Linear separability was achieved. Applying the FNN XOR function to optimizing subsets of data -- Summary -- Questions -- Further reading -- Chapter 9: Abstract Image Classification with Convolutional Neural Networks (CNNs) -- Introducing CNNs -- Defining a CNN -- Initializing the CNN -- Adding a 2D convolution layer -- Kernel -- Shape -- ReLU -- Pooling -- Next convolution and pooling layer -- Flattening -- Dense layers -- Dense activation functions -- Training a CNN model -- The goal -- Compiling the model -- The loss function -- The Adam optimizer -- Metrics -- The training dataset -- Data augmentation -- Loading the data -- The testing dataset -- Data augmentation on the testing dataset -- Loading the data -- Training with the classifier -- Saving the model -- Next steps -- Summary -- Questions -- Further reading and references -- Chapter 10: Conceptual Representation Learning -- Generating profit with transfer learning -- The motivation behind transfer learning -- Inductive thinking -- Inductive abstraction -- The problem AI needs to solve -- The gap concept -- Loading the trained TensorFlow 2.x model -- Loading and displaying the model -- Loading the model to use it -- Defining a strategy -- Making the model profitable by using it for another problem -- Domain learning -- How to use the programs -- The trained models used in this section -- The trained model program -- Gap - loaded or underloaded -- Gap - jammed or open lanes -- Gap datasets and subsets -- Generalizing the (the gap conceptual dataset) -- The motivation of conceptual representation learning metamodels applied to dimensionality -- The curse of dimensionality -- The blessing of dimensionality -- Summary -- Questions -- Further reading -- Chapter 11: Combining Reinforcement Learning and Deep Learning -- Planning and scheduling today and tomorrow -- A real-time manufacturing process. Amazon must expand its services to face competition -- A real-time manufacturing revolution -- CRLMM applied to an automated apparel manufacturing process -- An apparel manufacturing process -- Training the CRLMM -- Generalizing the unit training dataset -- Food conveyor belt processing - positive p and negative n gaps -- Running a prediction program -- Building the RL-DL-CRLMM -- A circular process -- Implementing a CNN-CRLMM to detect gaps and optimize -- Q-learning - MDP -- MDP inputs and outputs -- The optimizer -- The optimizer as a regulator -- Finding the main target for the MDP function -- A circular model - a stream-like system that never starts nor ends -- Summary -- Questions -- Further reading -- Chapter 12: AI and the Internet of Things (IoT) -- The public service project -- Setting up the RL-DL-CRLMM model -- Applying the model of the CRLMM -- The dataset -- Using the trained model -- Adding an SVM function -- Motivation - using an SVM to increase safety levels -- Definition of a support vector machine -- Python function -- Running the CRLMM -- Finding a parking space -- Deciding how to get to the parking lot -- Support vector machine -- The itinerary graph -- The weight vector -- Summary -- Questions -- Further reading -- Chapter 13: Visualizing Networks with TensorFlow 2.x and TensorBoard -- Exploring the output of the layers of a CNN in two steps with TensorFlow -- Building the layers of a CNN -- Processing the visual output of the layers of a CNN -- Analyzing the visual output of the layers of a CNN -- Analyzing the accuracy of a CNN using TensorBoard -- Getting started with Google Colaboratory -- Defining and training the model -- Introducing some of the measurements -- Summary -- Questions -- Further reading. Chapter 14: Preparing the Input of Chatbots with Restricted Boltzmann Machines (RBMs) and Principal Component Analysis (PCA). |
| Record Nr. | UNINA-9910780786103321 |
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Rothman Denis
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| Birmingham : , : Packt Publishing, Limited, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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