| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNINA9910369899703321 |
|
|
Autore |
Norris Donald J |
|
|
Titolo |
Machine Learning with the Raspberry Pi : Experiments with Data and Computer Vision / / by Donald J. Norris |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Berkeley, CA : , : Apress : , : Imprint : Apress, , 2020 |
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
|
|
Edizione |
[1st ed. 2020.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (571 pages) |
|
|
|
|
|
|
Collana |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Computer input-output equipment |
Hardware and Maker |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Note generali |
|
|
|
|
|
|
Nota di bibliografia |
|
Includes bibliographical references. |
|
|
|
|
|
|
Nota di contenuto |
|
Chapter 1: Introduction to Machine Learning (ML) with the Raspberry Pi (RasPi) -- Chapter 2: Exploration of ML data models: Part 1 -- Chapter 3: Exploration of ML data models: Part 2 -- Chapter 4: Preparation for Deep Learning -- Chapter 5: Practical deep learning ANN demonstrations -- Chapter 6: CNN demonstrations -- Chapter 7: Predictions using ANNs and CNNs -- Chapter 8: Predictions using CNNs and MLPs for medical research -- Chapter 9: Reinforcement Learning. . |
|
|
|
|
|
|
|
|
Sommario/riassunto |
|
Using the Pi Camera and a Raspberry Pi board, expand and replicate interesting machine learning (ML) experiments. This book provides a solid overview of ML and a myriad of underlying topics to further explore. Non-technical discussions temper complex technical explanations to make the hottest and most complex topic in the hobbyist world of computing understandable and approachable. Machine learning, also commonly referred to as deep learning (DL), is currently being integrated into a multitude of commercial products as well as widely being used in industrial, medical, and military applications. It is hard to find any modern human activity, which has not been "touched" by artificial intelligence (AI) applications. Building on the concepts first presented in Beginning Artificial Intelligence with the Raspberry Pi, you’ll go beyond simply understanding the concepts of AI into working with real machine learning experiments and applying practical deep learning concepts to experiments with the Pi board and |
|
|
|
|
|
|
|
|
|
|
|
|
|
computer vision. What you learn with Machine Learning with the Raspberry Pi can then be moved on to other platforms to go even further in the world of AI and ML to better your hobbyist or commercial projects. |
|
|
|
|
|
|
2. |
Record Nr. |
UNINA9910483320603321 |
|
|
Autore |
Brugière Pierre |
|
|
Titolo |
Quantitative Portfolio Management : with Applications in Python / / by Pierre Brugière |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
|
|
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
Edizione |
[1st ed. 2020.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (XII, 205 p. 23 illus., 22 illus. in color.) |
|
|
|
|
|
|
Collana |
|
Springer Texts in Business and Economics, , 2192-4341 |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Social sciences—Mathematics |
Statistics |
Application software |
Mathematics in Business, Economics and Finance |
Statistics in Business, Management, Economics, Finance, Insurance |
Computer and Information Systems Applications |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Nota di contenuto |
|
Returns and the Gaussian Hypothesis -- Utility Functions and the Theory of Choice -- The Markowitz Framework -- Markowitz Without a Risk-Free Asset -- Markowitz with a Risk-Free Asset -- Performance and Diversification Indicators -- Risk Measures and Capital Allocation -- Factor Models -- Identification of the Factors -- Exercises and Problems. |
|
|
|
|
|
|
|
|
Sommario/riassunto |
|
This self-contained book presents the main techniques of quantitative portfolio management and associated statistical methods in a very didactic and structured way, in a minimum number of pages. The concepts of investment portfolios, self-financing portfolios and absence of arbitrage opportunities are extensively used and enable the |
|
|
|
|
|
|
|
|
|
|
translation of all the mathematical concepts in an easily interpretable way. All the results, tested with Python programs, are demonstrated rigorously, often using geometric approaches for optimization problems and intrinsic approaches for statistical methods, leading to unusually short and elegant proofs. The statistical methods concern both parametric and non-parametric estimators and, to estimate the factors of a model, principal component analysis is explained. The presented Python code and web scraping techniques also make it possible to test the presented concepts on market data. This book will be useful for teaching Masters students and for professionals in asset management, and will be of interest to academics who want to explore a field in which they are not specialists. The ideal pre-requisites consist of undergraduate probability and statistics and a familiarity with linear algebra and matrix manipulation. Those who want to run the code will have to install Python on their pc, or alternatively can use Google Colab on the cloud. Professionals will need to have a quantitative background, being either portfolio managers or risk managers, or potentially quants wanting to double check their understanding of the subject. |
|
|
|
|
|
| |