Manage Your Own Learning Analytics: Implement a Rasch Modelling Approach, McKay Elspeth
Автор: Kulkarni Название: Time Series Algorithms Recipes ISBN: 1484289773 ISBN-13(EAN): 9781484289778 Издательство: Springer Рейтинг: Цена: 4268.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average). Next, you'll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations. After finishing this book, you will have a foundational understanding of various concepts relating to time series and its implementation in Python. What You Will Learn * Implement various techniques in time series analysis using Python. * Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average) and ARIMA (autoregressive integrated moving-average) for time series forecasting * Understand univariate and multivariate modeling for time series forecasting * Forecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory) Who This Book Is For Data Scientists, Machine Learning Engineers, and software developers interested in time series analysis.
Автор: Parisi, Alessandro Название: Hands-on artificial intelligence for cybersecurity ISBN: 1789804027 ISBN-13(EAN): 9781789804027 Издательство: Неизвестно Рейтинг: Цена: 9010.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: If you wish to design smart, threat-proof cybersecurity systems using trending AI tools and techniques, then this book is for you. With this book, you will learn to develop intelligent systems that can detect suspicious patterns and attacks, thereby allowing you to protect your network and corporate assets.
Описание: Chapter 1: Getting Started with Python 3 and Jupyter NotebookChapter Goal: Introduce the reader to the basics of Python Programming language, philosophy, and installation. We will also learn how to install it on various platforms. This chapter also introduces the readers to Python programming with Jupyter Notebook. In the end, we will also have a brief overview of the constituent libraries of sciPy stack.No of pages - 30Sub -Topics1. Introduction to the Python programming language2. History of Python3. Python enhancement proposals (PEPs)4. Philosophy of Python5. Real life applications of Python6. Installing Python on various platforms (Windows and Debian Linux Flavors)7. Python modes (Interactive and Script)8. Pip (pip installs python)9. Introduction to the scientific Python ecosystem10. Overview of Jupyter Notebook11. Installation of Jupyter Notebook12. Running code in Jupyter Notebook Chapter 2: Getting Started with NumPyChapter Goal: Get started with NumPy Ndarrays and the basics of NumPy library. The chapter covers the instructions for installation and basic usage of NumPy.No of pages: 10Sub - Topics: 1. Introduction to NumPy2. Install NumPy with pip33. Indexing and Slicing of ndarrays4. Properties of ndarrays5. Constants in NumPy6. Datatypes in datatypes Chapter 3: Introduction to Data VisualizationChapter goal - In this chapter, we will discuss the various ndarray creation routines available in NumPy. We will also get started with Visualizations with Matplotlib. We will learn how to visualize the various numerical ranges with Matplotlib.No of pages: 15Sub - Topics: 1. Ones and zeros2. Matrices3. Introduction to Matplotlib4. Running Matplotlib programs in Jupyter Notebook and the script mode5. Numerical ranges and visualizations Chapter 4: Introduction to Pandas Chapter goal - Get started with Pandas data structuresNo of pages: 10Sub - Topics: 1. Install Pandas2. What is Pandas3. Introduction to series4. Introduction to dataframesa) Plain Text Fileb) CSVc) Handling excel filed) NumPy file formate) NumPy CSV file readingf) Matplotlib Cbookg) Read CSVh) Read Exceli) Read JSONj) Picklek) Pandas and webl) Read SQLm) Clipboard Chapter 5: Introduction to Machine Learning with Scikit-LearnChapter goal - Get acquainted with machine learning basics and scikit-Learn libraryNo of pages: 101. What is machine learning, offline and online processes2. Supervised/unsupervised methods3. Overview of scikit learn library, APIs4. Dataset loading, generated datasets Chapter 6: Preparing Data for Machine LearningChapter Goal: Clean, vectorize and transform dataNo of Pages: 151. Type of data variables2. Vectorization3. Normalization4. Processing text and images Chapter 7: Supervised Learning Methods - 1Chapter Goal: Learn and implement classification and regression algorithmsNo of Pages: 301. Regression and classification, multiclass, multilabel classification2. K-nearest neighbors3. Linear regression, understanding parameters4. Logistic regression5. Decision trees Chapter 8: Tuning Supervised L
Автор: Jadon Shruti, Garg Ankush Название: Hands-On One-shot Learning with Python ISBN: 1838825460 ISBN-13(EAN): 9781838825461 Издательство: Неизвестно Рейтинг: Цена: 8458.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is a step by step guide to one-shot learning using Python-based libraries. It is designed to help you understand and design models that can learn information about your data from one, or only a few, training examples. You will also learn to apply these techniques with real-world examples and datasets for classification and regression.
Автор: Pramod Singh; Avinash Manure Название: Learn TensorFlow 2.0 ISBN: 1484255607 ISBN-13(EAN): 9781484255605 Издательство: Springer Рейтинг: Цена: 5487.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Learn TensorFlow 2.0 Chapter 1: TensorFlow 2.0 - An Introduction Chapter Goal: Introducing TensorFlow, major features, version 2.0 release. Chapter 2: Supervised Learning with TensorFlow 2.0Chapter Goal: Implementation of linear, logistic, SVM (Support Vector Machines) and random forest using TensorFlow. Chapter 3: Neural Networks and Deep Learning with TensorFlow 2.0Chapter Goal: Introduction to neural networks, deep learning and implementation using TensorFlow This chapter offers a detailed view of building Deep Learning models for various applications such as Forecasting using TensorFlow 2.0. The chapter also introduces optimization approaches and the techniques for hyper parameter tuning. Chapter 4: Images with TensorFlow 2.0Chapter Goal: TensorFlow 2.0 for images. This chapter focuses on building deep learning based models for image classification using TensorFlow 2.0. It covers advanced techniques such as GANs and transfer learning to image processing and classifications Chapter 5: Sequence to Sequence Modeling with TensorFlow 2.0 Chapter Goal: To understand sequence modeling using TensorFlow 2.0. This chapter covers the process of using different neural networks for NLP based tasks in TensorFlow 2.0. This includes sequence to sequence prediction, text translation using deep learning in TensorFlow 2.0 Chapter 6: TensorFlow 2.0 Models in Productionization Chapter Goal: Implementation of distributed training using TensorFlow. This chapter covers the process of scaling up the machine learning model training by implementing distributed training of TensorFlow models and deploying those models into production using TensorFlow serving layer
Автор: Ankan Ankur, Panda Abinash Название: Hands-On Markov Models with Python ISBN: 1788625447 ISBN-13(EAN): 9781788625449 Издательство: Неизвестно Рейтинг: Цена: 7539.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book will help you become familiar with HMMs and different inference algorithms by working on real-world problems. You will start with an introduction to the basic concepts of Markov chains, Markov processes and then delve deeper into understanding hidden Markov models and its types using practical examples.
Автор: Karim MD Rezaul Название: Java Deep Learning Projects ISBN: 178899745X ISBN-13(EAN): 9781788997454 Издательство: Неизвестно Рейтинг: Цена: 10666.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: You will build full-fledged, deep learning applications with Java and different open-source libraries. Master numerical computing, deep learning, and the latest Java programming features to carry out complex advanced tasks. This book is filled with best practices/tips after every project to help you optimize your deep learning models with ease.
Автор: Mishra Название: Explainable AI Recipes ISBN: 1484290283 ISBN-13(EAN): 9781484290286 Издательство: Springer Рейтинг: Цена: 4268.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Understand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning models. This book utilizes a problem-solution approach to explaining machine learning models and their algorithms. The book starts with model interpretation for supervised learning linear models, which includes feature importance, partial dependency analysis, and influential data point analysis for both classification and regression models. Next, it explains supervised learning using non-linear models and state-of-the-art frameworks such as SHAP values/scores and LIME for local interpretation. Explainability for time series models is covered using LIME and SHAP, as are natural language processing-related tasks such as text classification, and sentiment analysis with ELI5, and ALIBI. The book concludes with complex model classification and regression-like neural networks and deep learning models using the CAPTUM framework that shows feature attribution, neuron attribution, and activation attribution. After reading this book, you will understand AI and machine learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses. What You Will Learn * Create code snippets and explain machine learning models using Python * Leverage deep learning models using the latest code with agile implementations * Build, train, and explain neural network models designed to scale * Understand the different variants of neural network models Who This Book Is For AI engineers, data scientists, and software developers interested in XAI
Описание: Chapter 1: Optimization and neural networks Subtopics: How to read the book Introduction to the book Chapter 2: Hands-on with One Single NeuronSubtopics: Overview of optimization A definition of learning Constrained vs. unconstrained optimization Absolute and local minima Optimization algorithms with focus on Gradient Descent Variations of Gradient Descent (mini-batch and stochastic) How to choose the right mini-batch size Chapter 3: Feed Forward Neural NetworksSubtopics: A short introduction to matrix algebra Activation functions (identity, sigmoid, tanh, swish, etc.) Implementation of one neuron in Keras Linear regression with one neuron Logistic regression with one neuron Chapter 4: RegularizationSubtopics: Matrix formalism Softmax activation function Overfitting and bias-variance discussion How to implement a fully conneted network with Keras Multi-class classification with the Zalando dataset in Keras Gradient descent variation in practice with a real dataset Weight initialization How to compare the complexity of neural networks How to estimate memory used by neural networks in Keras Chapter 5: Advanced OptimizersSubtopics: An introduction to regularization l_p norm l_2 regularization Weight decay when using regularization Dropout Early Stopping Chapter 6Chapter Title: Hyper-Parameter tuningSubtopics: Exponentially weighted averages Momentum RMSProp Adam Comparison of optimizers Chapter 7Chapter Title: Convolutional Neural NetworksSubtopics: Introduction to Hyper-parameter tuning Black box optimization Grid Search Random Search Coarse to fine optimization Sampling on logarithmic scale Bayesian optimisation Chapter 8Chapter Title: Brief Introduction to Recurrent Neural NetworksSubtopics: Theory of convolution Pooling and padding Building blocks of a CNN Implementation of a CNN with Keras Introduction to recurrent neural networks Implementation of a RNN with Keras Chapter 9: AutoencodersSubtopics: Feed Forward Autoencoders Loss function in autoencoders Reconstruction error Application of autoencoders: dimensionality reduction Application of autoencoders: Classification with latent features Curse of dimensionality Denoising autoencoders Autoencoders with CNN Chapter 10: Metric AnalysisSubtopics: Human level performance and Bayes error Bias Metric analysis diagram Training set overfitting How to split your dataset Unbalanced dataset: what can happen K-fold cross validation Manual metric analysis: an example Chapter 11 Chapter Title: General Adversarial Networks (GANs)Subtopics: Introduction to GANs The building blocks of GANs An example of implementation of GANs in Keras APPENDIX 1: Introduction to KerasSubtopics: Sequential model Keras Layers Funct
Автор: Ramo Klevis Название: Hands-On Java Deep Learning for Computer Vision ISBN: 1789613965 ISBN-13(EAN): 9781789613964 Издательство: Неизвестно Рейтинг: Цена: 6435.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book will take you through the process of efficiently training deep neural networks in Java for Computer Vision-related tasks. You will build real-world applications ranging from simple Java handwritten digit recognition models to real-time autonomous car driving systems and face recognition models using the popular Java-based libraries.
Автор: Ayyadevara, V Kishore Название: Pro machine learning algorithms ISBN: 1484235630 ISBN-13(EAN): 9781484235638 Издательство: Springer Рейтинг: Цена: 7927.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a practical understanding of all the levers that can be tuned in a model, before implementing the models in Python/R.
You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic regression; k-means clustering; PCA; recommender system; decision tree; random forest; GBM; and neural networks. You will also be exposed to the latest in deep learning through CNNs, RNNs, and word2vec for text mining. You will be learning not only the algorithms, but also the concepts of feature engineering to maximize the performance of a model. You will see the theory along with case studies, such as sentiment classification, fraud detection, recommender systems, and image recognition, so that you get the best of both theory and practice for the vast majority of the machine learning algorithms used in industry. Along with learning the algorithms, you will also be exposed to running machine-learning models on all the major cloud service providers.
You are expected to have minimal knowledge of statistics/software programming and by the end of this book you should be able to work on a machine learning project with confidence.
What You Will Learn
Get an in-depth understanding of all the major machine learning and deep learning algorithms Fully appreciate the pitfalls to avoid while building modelsImplement machine learning algorithms in the cloud Follow a hands-on approach through case studies for each algorithmGain the tricks of ensemble learning to build more accurate modelsDiscover the basics of programming in R/Python and the Keras framework for deep learning
Who This Book Is For
Business analysts/ IT professionals who want to transition into data science roles. Data scientists who want to solidify their knowledge in machine learning.
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