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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 |
Rothman Denis
|
||
| Birmingham : , : Packt Publishing, Limited, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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-9910818902203321 |
|
Rothman Denis
|
||
| Birmingham : , : Packt Publishing, Limited, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial Intelligence By Example [[electronic resource] /] / Rothman, Denis
| Artificial Intelligence By Example [[electronic resource] /] / Rothman, Denis |
| Autore | Rothman Denis |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Packt Publishing, , 2018 |
| Descrizione fisica | 1 online resource (490 pages) |
| Soggetto genere / forma | Electronic books. |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910467221203321 |
|
Rothman Denis
|
||
| Packt Publishing, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence by example [[electronic resource] : develop machine intelligence from scratch using real artificial intelligence use cases /] / Denis Rothman
| Artificial intelligence by example [[electronic resource] : develop machine intelligence from scratch using real artificial intelligence use cases /] / Denis Rothman |
| Autore | Rothman Denis |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Birmingham, UK : , : Packt Publishing, , 2018 |
| Descrizione fisica | 1 online resource (1 volume) : illustrations |
| Soggetto topico |
Artificial intelligence - Data processing
Application software - Development Machine learning Neural networks (Computer science) Python (Computer program language) Cloud computing |
| Soggetto genere / forma | Electronic books. |
| ISBN | 9781788990028 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910548221403321 |
|
Rothman Denis
|
||
| Birmingham, UK : , : Packt Publishing, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence by example : develop machine intelligence from scratch using real artificial intelligence use cases / / Denis Rothman
| Artificial intelligence by example : develop machine intelligence from scratch using real artificial intelligence use cases / / Denis Rothman |
| Autore | Rothman Denis |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Birmingham : , : Packt, , 2018 |
| Descrizione fisica | 1 online resource (490 pages) |
| Disciplina | 006.3 |
| Soggetto topico | Artificial intelligence |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910794639003321 |
|
Rothman Denis
|
||
| Birmingham : , : Packt, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence by example : develop machine intelligence from scratch using real artificial intelligence use cases / / Denis Rothman
| Artificial intelligence by example : develop machine intelligence from scratch using real artificial intelligence use cases / / Denis Rothman |
| Autore | Rothman Denis |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Birmingham : , : Packt, , 2018 |
| Descrizione fisica | 1 online resource (490 pages) |
| Disciplina | 006.3 |
| Soggetto topico | Artificial intelligence |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910808496303321 |
|
Rothman Denis
|
||
| Birmingham : , : Packt, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||