Learning Processing

Learning Processing
Author: Daniel Shiffman
Publsiher: Newnes
Total Pages: 564
Release: 2015-09-09
ISBN 10: 0123947928
ISBN 13: 9780123947925
Language: EN, FR, DE, ES & NL

Learning Processing Book Review:

Learning Processing, Second Edition, is a friendly start-up guide to Processing, a free, open-source alternative to expensive software and daunting programming languages. Requiring no previous experience, this book is for the true programming beginner. It teaches the basic building blocks of programming needed to create cutting-edge graphics applications including interactive art, live video processing, and data visualization. Step-by-step examples, thorough explanations, hands-on exercises, and sample code, supports your learning curve. A unique lab-style manual, the book gives graphic and web designers, artists, and illustrators of all stripes a jumpstart on working with the Processing programming environment by providing instruction on the basic principles of the language, followed by careful explanations of select advanced techniques. The book has been developed with a supportive learning experience at its core. From algorithms and data mining to rendering and debugging, it teaches object-oriented programming from the ground up within the fascinating context of interactive visual media. This book is ideal for graphic designers and visual artists without programming background who want to learn programming. It will also appeal to students taking college and graduate courses in interactive media or visual computing, and for self-study. A friendly start-up guide to Processing, a free, open-source alternative to expensive software and daunting programming languages No previous experience required—this book is for the true programming beginner! Step-by-step examples, thorough explanations, hands-on exercises, and sample code supports your learning curve

Learning Processing

Learning Processing
Author: Daniel Shiffman
Publsiher: Morgan Kaufmann
Total Pages: 472
Release: 2009-04-17
ISBN 10: 9780080920061
ISBN 13: 0080920063
Language: EN, FR, DE, ES & NL

Learning Processing Book Review:

The free, open-source Processing programming language environment was created at MIT for people who want to develop images, animation, and sound. Based on the ubiquitous Java, it provides an alternative to daunting languages and expensive proprietary software. This book gives graphic designers, artists and illustrators of all stripes a jump start to working with processing by providing detailed information on the basic principles of programming with the language, followed by careful, step-by-step explanations of select advanced techniques. The author teaches computer graphics at NYU's Tisch School of the Arts, and his book has been developed with a supportive learning experience at its core. From algorithms and data mining to rendering and debugging, it teaches object-oriented programming from the ground up within the fascinating context of interactive visual media. Previously announced as "Pixels, Patterns, and Processing" *A guided journey from the very basics of computer programming through to creating custom interactive 3D graphics *Step-by-step examples, approachable language, exercises, and LOTS of sample code support the reader's learning curve *Includes lessons on how to program live video, animated images and interactive sound

Learning Approaches in Signal Processing

Learning Approaches in Signal Processing
Author: Wan-Chi Siu,Lap-Pui Chau,Liang Wang,Tieniu Tang
Publsiher: CRC Press
Total Pages: 654
Release: 2018-12-07
ISBN 10: 0429592264
ISBN 13: 9780429592263
Language: EN, FR, DE, ES & NL

Learning Approaches in Signal Processing Book Review:

Coupled with machine learning, the use of signal processing techniques for big data analysis, Internet of things, smart cities, security, and bio-informatics applications has witnessed explosive growth. This has been made possible via fast algorithms on data, speech, image, and video processing with advanced GPU technology. This book presents an up-to-date tutorial and overview on learning technologies such as random forests, sparsity, and low-rank matrix estimation and cutting-edge visual/signal processing techniques, including face recognition, Kalman filtering, and multirate DSP. It discusses the applications that make use of deep learning, convolutional neural networks, random forests, etc. The applications include super-resolution imaging, fringe projection profilometry, human activities detection/capture, gesture recognition, spoken language processing, cooperative networks, bioinformatics, DNA, and healthcare.

Neural Modeling of Speech Processing and Speech Learning

Neural Modeling of Speech Processing and Speech Learning
Author: Bernd J. Kröger,Trevor Bekolay
Publsiher: Springer
Total Pages: 280
Release: 2019-07-11
ISBN 10: 3030158535
ISBN 13: 9783030158538
Language: EN, FR, DE, ES & NL

Neural Modeling of Speech Processing and Speech Learning Book Review:

This book explores the processes of spoken language production and perception from a neurobiological perspective. After presenting the basics of speech processing and speech acquisition, a neurobiologically-inspired and computer-implemented neural model is described, which simulates the neural processes of speech processing and speech acquisition. This book is an introduction to the field and aimed at students and scientists in neuroscience, computer science, medicine, psychology and linguistics.

Biosignal Processing and Classification Using Computational Learning and Intelligence

Biosignal Processing and Classification Using Computational Learning and Intelligence
Author: Alejandro Antonio Torres Garcia,Carlos Alberto Reyes Garcia,Luis Villasenor-Pineda,Omar Mendoza-Montoya
Publsiher: Academic Press
Total Pages: 536
Release: 2021-09-18
ISBN 10: 0128204281
ISBN 13: 9780128204283
Language: EN, FR, DE, ES & NL

Biosignal Processing and Classification Using Computational Learning and Intelligence Book Review:

Biosignal Processing and Classification Using Computational Learning and Intelligence: Principles, Algorithms and Applications posits an approach for biosignal processing and classification using computational learning and intelligence, highlighting that the term biosignal refers to all kinds of signals that can be continuously measured and monitored in living beings. The book is composed of five relevant parts. Part One is an introduction to biosignals and Part Two describes the relevant techniques for biosignal processing, feature extraction and feature selection/dimensionality reduction. Part Three presents the fundamentals of computational learning (machine learning). Then, the main techniques of computational intelligence are described in Part Four. The authors focus primarily on the explanation of the most used methods in the last part of this book, which is the most extensive portion of the book. This part consists of a recapitulation of the newest applications and reviews in which these techniques have been successfully applied to the biosignals’ domain, including EEG-based Brain-Computer Interfaces (BCI) focused on P300 and Imagined Speech, emotion recognition from voice and video, leukemia recognition, infant cry recognition, EEGbased ADHD identification among others. Provides coverage of the fundamentals of signal processing, including sensing the heart, sending the brain, sensing human acoustic, and sensing other organs Includes coverage biosignal pre-processing techniques such as filtering, artifiact removal, and feature extraction techniques such as Fourier transform, wavelet transform, and MFCC Covers the latest techniques in machine learning and computational intelligence, including Supervised Learning, common classifiers, feature selection, dimensionality reduction, fuzzy logic, neural networks, Deep Learning, bio-inspired algorithms, and Hybrid Systems Written by engineers to help engineers, computer scientists, researchers, and clinicians understand the technology and applications of computational learning to biosignal processing

Representation Learning for Natural Language Processing

Representation Learning for Natural Language Processing
Author: Zhiyuan Liu,Yankai Lin,Maosong Sun
Publsiher: Springer Nature
Total Pages: 334
Release: 2020-01-01
ISBN 10: 9811555737
ISBN 13: 9789811555732
Language: EN, FR, DE, ES & NL

Representation Learning for Natural Language Processing Book Review:

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.

Machine Learning in Translation Corpora Processing

Machine Learning in Translation Corpora Processing
Author: Krzysztof Wolk
Publsiher: CRC Press
Total Pages: 264
Release: 2019-02-25
ISBN 10: 0429588836
ISBN 13: 9780429588839
Language: EN, FR, DE, ES & NL

Machine Learning in Translation Corpora Processing Book Review:

This book reviews ways to improve statistical machine speech translation between Polish and English. Research has been conducted mostly on dictionary-based, rule-based, and syntax-based, machine translation techniques. Most popular methodologies and tools are not well-suited for the Polish language and therefore require adaptation, and language resources are lacking in parallel and monolingual data. The main objective of this volume to develop an automatic and robust Polish-to-English translation system to meet specific translation requirements and to develop bilingual textual resources by mining comparable corpora.

Learning to Rank for Information Retrieval and Natural Language Processing

Learning to Rank for Information Retrieval and Natural Language Processing
Author: Hang Li
Publsiher: Morgan & Claypool Publishers
Total Pages: 121
Release: 2014-10-01
ISBN 10: 1627055851
ISBN 13: 9781627055857
Language: EN, FR, DE, ES & NL

Learning to Rank for Information Retrieval and Natural Language Processing Book Review:

Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings. Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting based, and Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include: PRank, OC SVM, McRank, Ranking SVM, IR SVM, GBRank, RankNet, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, LambdaRank, LambdaMART, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed. Table of Contents: Learning to Rank / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work

Natural Language Processing

Natural Language Processing
Author: Yue Zhang,Zhiyang Teng
Publsiher: Cambridge University Press
Total Pages: 484
Release: 2021-01-07
ISBN 10: 1108420214
ISBN 13: 9781108420211
Language: EN, FR, DE, ES & NL

Natural Language Processing Book Review:

This undergraduate textbook introduces essential machine learning concepts in NLP in a unified and gentle mathematical framework.

Natural Language Processing with PyTorch

Natural Language Processing with PyTorch
Author: Delip Rao,Brian McMahan
Publsiher: O'Reilly Media
Total Pages: 256
Release: 2019-01-22
ISBN 10: 1491978201
ISBN 13: 9781491978207
Language: EN, FR, DE, ES & NL

Natural Language Processing with PyTorch Book Review:

Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library. Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. Each chapter includes several code examples and illustrations. Explore computational graphs and the supervised learning paradigm Master the basics of the PyTorch optimized tensor manipulation library Get an overview of traditional NLP concepts and methods Learn the basic ideas involved in building neural networks Use embeddings to represent words, sentences, documents, and other features Explore sequence prediction and generate sequence-to-sequence models Learn design patterns for building production NLP systems

Getting Started with Processing py

Getting Started with Processing py
Author: Allison Parrish,Ben Fry,Casey Reas
Publsiher: Maker Media, Inc.
Total Pages: 242
Release: 2016-05-11
ISBN 10: 1457186799
ISBN 13: 9781457186790
Language: EN, FR, DE, ES & NL

Getting Started with Processing py Book Review:

Processing opened up the world of programming to artists, designers, educators, and beginners. The Processing.py Python implementation of Processing reinterprets it for today's web. This short book gently introduces the core concepts of computer programming and working with Processing. Written by the co-founders of the Processing project, Reas and Fry, along with co-author Allison Parrish, Getting Started with Processing.py is your fast track to using Python's Processing mode.

Transfer Learning for Natural Language Processing

Transfer Learning for Natural Language Processing
Author: Paul Azunre
Publsiher: Simon and Schuster
Total Pages: 272
Release: 2021-08-31
ISBN 10: 163835099X
ISBN 13: 9781638350996
Language: EN, FR, DE, ES & NL

Transfer Learning for Natural Language Processing Book Review:

Build custom NLP models in record time by adapting pre-trained machine learning models to solve specialized problems. Summary In Transfer Learning for Natural Language Processing you will learn: Fine tuning pretrained models with new domain data Picking the right model to reduce resource usage Transfer learning for neural network architectures Generating text with generative pretrained transformers Cross-lingual transfer learning with BERT Foundations for exploring NLP academic literature Training deep learning NLP models from scratch is costly, time-consuming, and requires massive amounts of data. In Transfer Learning for Natural Language Processing, DARPA researcher Paul Azunre reveals cutting-edge transfer learning techniques that apply customizable pretrained models to your own NLP architectures. You’ll learn how to use transfer learning to deliver state-of-the-art results for language comprehension, even when working with limited label data. Best of all, you’ll save on training time and computational costs. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build custom NLP models in record time, even with limited datasets! Transfer learning is a machine learning technique for adapting pretrained machine learning models to solve specialized problems. This powerful approach has revolutionized natural language processing, driving improvements in machine translation, business analytics, and natural language generation. About the book Transfer Learning for Natural Language Processing teaches you to create powerful NLP solutions quickly by building on existing pretrained models. This instantly useful book provides crystal-clear explanations of the concepts you need to grok transfer learning along with hands-on examples so you can practice your new skills immediately. As you go, you’ll apply state-of-the-art transfer learning methods to create a spam email classifier, a fact checker, and more real-world applications. What's inside Fine tuning pretrained models with new domain data Picking the right model to reduce resource use Transfer learning for neural network architectures Generating text with pretrained transformers About the reader For machine learning engineers and data scientists with some experience in NLP. About the author Paul Azunre holds a PhD in Computer Science from MIT and has served as a Principal Investigator on several DARPA research programs. Table of Contents PART 1 INTRODUCTION AND OVERVIEW 1 What is transfer learning? 2 Getting started with baselines: Data preprocessing 3 Getting started with baselines: Benchmarking and optimization PART 2 SHALLOW TRANSFER LEARNING AND DEEP TRANSFER LEARNING WITH RECURRENT NEURAL NETWORKS (RNNS) 4 Shallow transfer learning for NLP 5 Preprocessing data for recurrent neural network deep transfer learning experiments 6 Deep transfer learning for NLP with recurrent neural networks PART 3 DEEP TRANSFER LEARNING WITH TRANSFORMERS AND ADAPTATION STRATEGIES 7 Deep transfer learning for NLP with the transformer and GPT 8 Deep transfer learning for NLP with BERT and multilingual BERT 9 ULMFiT and knowledge distillation adaptation strategies 10 ALBERT, adapters, and multitask adaptation strategies 11 Conclusions

Learning Machine Translation

Learning Machine Translation
Author: Cyril Goutte,Nicola Cancedda,Marc Dymetman,George Foster
Publsiher: MIT Press
Total Pages: 316
Release: 2009
ISBN 10: 0262072971
ISBN 13: 9780262072977
Language: EN, FR, DE, ES & NL

Learning Machine Translation Book Review:

How Machine Learning can improve machine translation: enabling technologies and new statistical techniques.

Prediction in Second Language Processing and Learning

Prediction in Second Language Processing and Learning
Author: Edith Kaan,Theres Grüter
Publsiher: John Benjamins Publishing Company
Total Pages: 234
Release: 2021-09-15
ISBN 10: 9027258945
ISBN 13: 9789027258946
Language: EN, FR, DE, ES & NL

Prediction in Second Language Processing and Learning Book Review:

There is ample evidence that language users, including second-language (L2) users, can predict upcoming information during listening and reading. Yet it is still unclear when, how, and why language users engage in prediction, and what the relation is between prediction and learning. This volume presents a collection of current research, insights, and directions regarding the role of prediction in L2 processing and learning. The contributions in this volume specifically address how different (L1-based) theoretical models of prediction apply to or may be expanded to account for L2 processing, report new insights on factors (linguistic, cognitive, social) that modulate L2 users’ engagement in prediction, and discuss the functions that prediction may or may not serve in L2 processing and learning. Taken together, this volume illustrates various fruitful approaches to investigating and accounting for differences in predictive processing within and across individuals, as well as across populations.

Machine Learning for Signal Processing

Machine Learning for Signal Processing
Author: Max A. Little
Publsiher: Oxford University Press
Total Pages: 384
Release: 2019-08-13
ISBN 10: 0191024317
ISBN 13: 9780191024313
Language: EN, FR, DE, ES & NL

Machine Learning for Signal Processing Book Review:

This book describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Taking a gradual approach, it builds up concepts in a solid, step-by-step fashion so that the ideas and algorithms can be implemented in practical software applications. Digital signal processing (DSP) is one of the 'foundational' engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance, and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference. DSP and statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered, yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts and algorithms in this important topic.

Learning Algorithms

Learning Algorithms
Author: P. Mars
Publsiher: CRC Press
Total Pages: 240
Release: 2018-01-18
ISBN 10: 1351082426
ISBN 13: 9781351082426
Language: EN, FR, DE, ES & NL

Learning Algorithms Book Review:

Over the past decade, interest in computational or non-symbolic artificial intelligence has grown. The algorithms involved have the ability to learn from past experience, and therefore have significant potential in the adaptive control of signals and systems. This book focuses on the theory and applications of learning algorithms-stochastic learning automata; artificial neural networks; and genetic algorithms, evolutionary strategies, and evolutionary programming. Hybrid combinations of various algorithms are also discussed.Chapter 1 provides a brief overview of the topics discussed and organization of the text. The first half of the book (Chapters 2 through 4) discusses the basic theory of the learning algorithms, with one chapter devoted to each type. In the second half (Chapters 5 through 7), the emphasis is on a wide range of applications drawn from adaptive signal processing, system identification, and adaptive control problems in telecommunication networks.Learning Algorithms: Theory and Applications in Signal Processing, Control and Communications is an excellent text for final year undergraduate and first year graduate students in engineering, computer science, and related areas. Professional engineers and everyone involved in the application of learning techniques in adaptive signal processing, control, and communications will find this text a valuable synthesis of theory and practical application of the most useful algorithms.

The Nature of Code

The Nature of Code
Author: Daniel Shiffman
Publsiher: Nature of Code
Total Pages: 498
Release: 2012
ISBN 10: 9780985930806
ISBN 13: 0985930802
Language: EN, FR, DE, ES & NL

The Nature of Code Book Review:

How can we capture the unpredictable evolutionary and emergent properties of nature in software? How can understanding the mathematical principles behind our physical world help us to create digital worlds? This book focuses on a range of programming strategies and techniques behind computer simulations of natural systems, from elementary concepts in mathematics and physics to more advanced algorithms that enable sophisticated visual results. Readers will progress from building a basic physics engine to creating intelligent moving objects and complex systems, setting the foundation for further experiments in generative design. Subjects covered include forces, trigonometry, fractals, cellular automata, self-organization, and genetic algorithms. The book's examples are written in Processing, an open-source language and development environment built on top of the Java programming language. On the book's website (http: //www.natureofcode.com), the examples run in the browser via Processing's JavaScript mode.

Speech Language Processing

Speech   Language Processing
Author: Dan Jurafsky
Publsiher: Pearson Education India
Total Pages: 908
Release: 2000-09
ISBN 10: 9788131716724
ISBN 13: 8131716724
Language: EN, FR, DE, ES & NL

Speech Language Processing Book Review:

Document Processing Using Machine Learning

Document Processing Using Machine Learning
Author: Sk Md Obaidullah,KC Santosh,Teresa Goncalves,Nibaran Das,Kaushik Roy
Publsiher: CRC Press
Total Pages: 168
Release: 2019-11-25
ISBN 10: 1000739538
ISBN 13: 9781000739534
Language: EN, FR, DE, ES & NL

Document Processing Using Machine Learning Book Review:

Document Processing Using Machine Learning aims at presenting a handful of resources for students and researchers working in the document image analysis (DIA) domain using machine learning since it covers multiple document processing problems. Starting with an explanation of how Artificial Intelligence (AI) plays an important role in this domain, the book further discusses how different machine learning algorithms can be applied for classification/recognition and clustering problems regardless the type of input data: images or text. In brief, the book offers comprehensive coverage of the most essential topics, including: · The role of AI for document image analysis · Optical character recognition · Machine learning algorithms for document analysis · Extreme learning machines and their applications · Mathematical foundation for Web text document analysis · Social media data analysis · Modalities for document dataset generation This book serves both undergraduate and graduate scholars in Computer Science/Information Technology/Electrical and Computer Engineering. Further, it is a great fit for early career research scientists and industrialists in the domain.

Natural Language Processing with Java

Natural Language Processing with Java
Author: Richard M. Reese,AshishSingh Bhatia
Publsiher: Packt Publishing Ltd
Total Pages: 318
Release: 2018-07-31
ISBN 10: 1788993063
ISBN 13: 9781788993067
Language: EN, FR, DE, ES & NL

Natural Language Processing with Java Book Review:

Explore various approaches to organize and extract useful text from unstructured data using Java Key Features Use deep learning and NLP techniques in Java to discover hidden insights in text Work with popular Java libraries such as CoreNLP, OpenNLP, and Mallet Explore machine translation, identifying parts of speech, and topic modeling Book Description Natural Language Processing (NLP) allows you to take any sentence and identify patterns, special names, company names, and more. The second edition of Natural Language Processing with Java teaches you how to perform language analysis with the help of Java libraries, while constantly gaining insights from the outcomes. You’ll start by understanding how NLP and its various concepts work. Having got to grips with the basics, you’ll explore important tools and libraries in Java for NLP, such as CoreNLP, OpenNLP, Neuroph, and Mallet. You’ll then start performing NLP on different inputs and tasks, such as tokenization, model training, parts-of-speech and parsing trees. You’ll learn about statistical machine translation, summarization, dialog systems, complex searches, supervised and unsupervised NLP, and more. By the end of this book, you’ll have learned more about NLP, neural networks, and various other trained models in Java for enhancing the performance of NLP applications. What you will learn Understand basic NLP tasks and how they relate to one another Discover and use the available tokenization engines Apply search techniques to find people, as well as things, within a document Construct solutions to identify parts of speech within sentences Use parsers to extract relationships between elements of a document Identify topics in a set of documents Explore topic modeling from a document Who this book is for Natural Language Processing with Java is for you if you are a data analyst, data scientist, or machine learning engineer who wants to extract information from a language using Java. Knowledge of Java programming is needed, while a basic understanding of statistics will be useful but not mandatory.