الصفحة 1
الصفحة 1
Statistical quantitative methods in finance : From theory to quantitative portfolio management
Explores the theoretical foundations of statistical models, from ordinary least squares (OLS) to the generalized method of moments (GMM) used in econometrics. additionally, the book delves into non-linear methods and bayesian approaches, which are becoming increasingly popular among practitioners thanks to advancements in computational resources. the book also offers valuable insights into quantitative portfolio management, showcasing how traditional data science tools can be enhanced with machine learning models. these enhancements are illustrated through real-world examples from finance and econometrics, accompanied by python code.
Songs generator
Songs generator app is a tool to generate lyrics, musi,and voice based on the given selected choices of users using Deep learning models. This app has multiple aims to achieve, the first aim is to generate lyrics to help a computer program capable of generating lyrics for given melodies, the deep learning model goal in this phase is to generate lyrics of the chosen mood say (happy, sad, etc. ), by training itself based on the given datasets of lyrics of various artists, but not identical to existing lyrics, it takes a sequence of words as an input and output a continuation of that sequence using our model. For the second phase, The goal of this model is to generate music by using a deep learning model that takes tokenized MIDI files as input and predicts the notes as an output the output of this model would be a midi file. For the last phase, we used a library to generate a voice that takes the output of the first model and the second model to generate a voice that is consistent with the lyrics and the music.
Practical Machine Learning for Streaming Data with Python : Design, Develop, and Validate Online Learning Models
A quick start guide for data scientists and machine learning engineers looking to implement machine learning models for streaming data with Python to generate real-time insights. You'll start with an introduction to streaming data, the various challenges associated with it, some of its real-world business applications, and various windowing techniques. You'll then examine incremental and online learning algorithms, and the concept of model evaluation with streaming data and get introduced to the Scikit-Multiflow framework in Python. This is followed by a review of the various change detection/concept drift detection algorithms and the implementation of various datasets using Scikit-Multiflow. Introduction to the various supervised and unsupervised algorithms for streaming data, and their implementation on various datasets using Python are also covered. You will: Understand machine learning with streaming data concepts / Review incremental and online learning / Develop models for detecting concept drift / Explore techniques for classification, regression, and ensemble learning in streaming data contexts / Apply best practices for debugging and validating machine learning models in streaming data context / Get introduced to other open-source frameworks for handling streaming data.
Oyooni: a deep learning-based arabic assistant for visually impaired people
Since the beginning of time, visually impaired people have been relying on other senses to interpret their surroundings and function properly, using mobility tools and white canes to guide them. About a decade ago, their quality of life was leveled up by the advances in deep learning and computer vision that were able to achieve state-of-the-art results to assist blind people in daily tasks. Unfortunately, these progresses are very limited in Arabic language. Hence, this project presents “Oyooni” (Arabic for “My Eyes”), a voice-powered Arabic assistant, that introduces several contributions to the Arabic language community, and showcases four deep learning models trained to be used by Arabic-speaking users, in the hopes of helping visually challenged people detect Syrian banknotes, caption scenes in Arabic, detect color and recognize Arabic written text.
Object detection with deep learning models : Principles and applications
Discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval / A structured overview of deep learning in object detection / A diversified collection of applications of object detection using deep neural networks / Emphasize agriculture and remote sensing domains / Exclusive discussion on moving object detection
Neural networks and deep learning
Covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems.
Modern deep learning for tabular data : Novel approaches to common modeling problems
Synthesizes and presents novel deep learning approaches to a seemingly unlikely domain - tabular data. Whether for finance, business, security, medicine, or countless other domain, deep learning can help mine and model complex patterns in tabular data - an incredibly ubiquitous form of structured data. Part I of the book offers a rigorous overview of machine learning principles, algorithms, and implementation skills relevant to holistically modeling and manipulating tabular data. Part II studies five dominant deep learning model designs - Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Attention and Transformers, and Tree-Rooted Networks - through both their 'default' usage and their application to tabular data. Part III compounds the power of the previously covered methods by surveying strategies and techniques to supercharge deep learning systems: autoencoders, deep data generation, meta-optimization, multi-model arrangement, and neural network interpretability.
Modern Deep Learning Design and Application Development : Versatile Tools to Solve Deep Learning Problems
Learn how to harness modern deep-learning methods in many contexts. Packed with intuitive theory, practical implementation methods, and deep-learning case studies, this book reveals how to acquire the tools you need to design and implement like a deep-learning architect. It covers tools deep learning engineers can use in a wide range of fields, from biology to computer vision to business. With nine in-depth case studies, this book will ground you in creative, real-world deep learning thinking. You will: Improve the performance of deep learning models by using pre-trained models, extracting rich features, and automating optimization. Compress deep learning models while maintaining performance. Reframe a wide variety of difficult problems and design effective deep learning solutions to solve them. Use the Keras framework, with some help from libraries like HyperOpt, TensorFlow, and PyTorch, to implement a wide variety of deep learning approaches.
Investment valuation and asset pricing : Models and methods
Offers an overview of original works on foundational asset pricing studies that follows their historical publication chronologically throughout the text. Each chapter stays close to the original works of these major authors, including quotations, examples, graphical exhibits, and empirical results. Additionally, it includes statistical concepts and methods as applied to finance. These statistical materials are crucial to learning asset pricing, which often applies statistical tests to evaluate different asset pricing models.
Inside deep learning : Math, algorithms, models
Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems. In Inside Deep Learning, you will learn how to: Implement deep learning with PyTorch Select the right deep learning components Train and evaluate a deep learning model Fine tune deep learning models to maximize performance Understand deep learning terminology Adapt existing PyTorch code to solve new problems Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped--you'll dive into math, theory, and practical applications. Everything is clearly explained in plain English.
Implementing machine learning for finance : A systematic approach to predictive risk and performance analysis for investment portfolios
Introduces pattern recognition and future price forecasting that exerts effects on time series analysis models, such as the Autoregressive Integrated Moving Average (ARIMA) model, Seasonal ARIMA (SARIMA) model, and Additive model, and it covers the Least Squares model and the Long Short-Term Memory (LSTM) model. It presents hidden pattern recognition and market regime prediction applying the Gaussian Hidden Markov Model. The book covers the practical application of the K-Means model in stock clustering. It establishes the practical application of the Variance-Covariance method and Simulation method (using Monte Carlo Simulation) for value at risk estimation. It also includes market direction classification using both the Logistic classifier and the Multilayer Perceptron classifier. Finally, the book presents performance and risk analysis for investment portfolios. You will: Understand the fundamentals of the financial market and algorithmic trading, as well as supervised and unsupervised learning models that are appropriate for systematic investment portfolio management / Know the concepts of feature engineering, data visualization, and hyperparameter optimization / Design, build, and test supervised and unsupervised ML and DL models / Discover seasonality, trends, and market regimes, simulating a change in the market and investment strategy problems and predicting market direction and prices / Structure and optimize an investment portfolio with preeminent asset classes and measure the / underlying risk
Hybrid Learning and Education ; 1st International Conference, ICHL 2008 Hong Kong, China, August 13-15, 2008 Proceedings
This book constitutes the refereed proceedings of the First International Conference on Hybrid Learning, ICHL 2008, held in Hong Kong, China, in August 2008.The 38 revised full papers presented together with 3 keynote lectures were carefully reviewed and selected from 142 submissions. The papers are organized in topical sections on hybrid education, model and pedagogies for hybrid learning, trends, pervasive learning, mobile and ubiquitous learning, hybrid learning experiences, hybrid learning systems, technologies, as well as contextual attitude and cultural effects.
Groupware : Design, Implementation, and Use ; 14th International Workshop, CRIWG 2008, Omaha, NE, USA, September 14-18, 2008, Revised Selected Papers
This book constitutes the refereed post-conference proceedings of the 14th International Workshop on Groupware: Design, Implementation, and Use, held in Omaha, Nebraska, USA, during September 14-18, 2008.The 30 papers presented were carefully reviewed and selected from numerous submission. The topics covered are groupware solutions, co-located groups, groupware for health care, collaborative systems development, collaborative emergency response, groupware approaches, patterns of collaboration.
Explainable Artificial Intelligence : An Introduction to Interpretable Machine Learning
Offers a curriculum for introducing interpretability to machine learning at every stage. The authors provide compelling examples that a core teaching practice like leading interpretive discussions can be taught and learned by teachers and sustained effort. And what better way to strengthen the quality of AI and Machine learning outcomes. I hope that this book will become a primer for teachers, data Science educators, and ML developers, and together we practice the art of interpretive machine learning.
Deep learning pipeline : Building a deep learning model with TensorFlow
Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. This book shows you how to build a deep learning pipeline for real-life TensorFlow projects. You'll learn what a pipeline is and how it works so you can build a full application easily and rapidly. Then troubleshoot and overcome basic Tensorflow obstacles to easily create functional apps and deploy well-trained models. Step-by-step and example-oriented instructions help you understand each step of the deep learning pipeline while you apply the most straightforward and effective tools to demonstrative problems and datasets.
Deep learning architecture and application
As one of the fastest-growing topics in machine learning, deep learning algorithms have achieved unprecedented success in recent years. Novel paradigms (such as contrastive learning and few-shot learning) in deep learning and rising neural network architectures (e.g., transformer and masked autoencoder) are dramatically changing the field of data-driven algorithms. More importantly, deep learning models are redefining the next generation of industrial applications spanning image recognition, speech processing, language translation, healthcare, and other sciences. For example, recent advances in deep representation learning are allowing us to learn about protein 3D structures, which sheds new light on fundamental medicine and biology along with potentially bringing in billions of dollars (e.g., in the pharmaceutical market).
Data science on the Google cloud platform : Implementing end-to-end real-time data pipelines : From ingest to machine learning
Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP. You'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own project on GCP, and discover how to solve data science problems in a transformative and more collaborative way. Employ best practices in building highly scalable data and ML pipelines on Google Cloud Automate and schedule data ingest using Cloud Run Create and populate a dashboard in Data Studio Build a real-time analytics pipeline using Pub/Sub, Dataflow, and BigQuery Conduct interactive data exploration with BigQuery Create a Bayesian model with Spark on Cloud Dataproc Forecast time series and do anomaly detection with BigQuery ML Aggregate within time windows with Dataflow Train explainable machine learning models with Vertex AI Operationalize ML with Vertex AI Pipelines
Marketing effectiveness : Applying marketing science for brand growth
Contrary to popular belief marketing effectiveness is not just about the measuring of ROI. The lens of effectiveness must be applied to all marketing mix elements, from strategy to pricing and product, to media and advertising. It's a strategic shift that demands robust evidence-based decisions and consistent application in order to grow. Written by leading marketing practitioner, Sorin Patilinet, this book enables mid-senior level marketers to integrate the scientific methods and advanced measurements required for true marketing effectiveness into their marketing strategies, in order to reap the benefits of strong customer understanding and developing decision-making processes for growth. Covering everything from neuroscience and its application to marketing to advanced analytics and machine learning models, this book provides a comprehensive practical guide for marketers.
Machine Learning Applications in Civil Engineering
Discusses machine learning and deep learning models for different civil engineering applications. These models work for stochastic methods wherein internal processing is done using randomized prototypes. The book explains various machine learning model designs that will assist researchers to design multi domain systems with maximum efficiency. It introduces Machine Learning and its applications to different Civil Engineering tasks, including Basic Machine Learning Models for data pre-processing, models for data representation, classification models for Civil Engineering Applications, Bioinspired Computing models for Civil Engineering, and their case studies.
Beginning deep learning with TensorFlow : Work with Keras, MNIST data sets, and advanced neural networks
Stats with an introduction to AI, where you’ll learn the history of neural networks and what sets deep learning apart from other varieties of machine learning. Discovery the variety of deep learning frameworks and set-up a deep learning development environment. Next, you’ll jump into simple classification programs for hand-writing analysis. Once you’ve tackled the basics of deep learning, you move on to TensorFlow 2 specifically. Find out what exactly a Tensor is and how to work with MNIST datasets. Finally, you’ll get into the heavy lifting of programming neural networks and working with a wide variety of neural network types such as GANs and RNNs. Deep Learning is a new area of Machine Learning research widely used in popular applications, such as voice assistant and self-driving cars. Work through the hands-on material in this book and become a TensorFlow programmer! You will: Develop using deep learning algorithms Build deep learning models using TensorFlow 2 Create classification systems and other, practical deep learning applications
