Classification And Prediction Projects With Machine Learning And Deep Learning
DOWNLOAD
Download Classification And Prediction Projects With Machine Learning And Deep Learning PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Classification And Prediction Projects With Machine Learning And Deep Learning book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Classification And Prediction Projects With Machine Learning And Deep Learning
DOWNLOAD
Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-02-06
Classification And Prediction Projects With Machine Learning And Deep Learning written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-06 with Computers categories.
PROJECT 1: DATA SCIENCE CRASH COURSE: Drinking Water Potability Classification and Prediction Using Machine Learning and Deep Learning with Python Access to safe drinking water is essential to health, a basic human right, and a component of effective policy for health protection. This is important as a health and development issue at a national, regional, and local level. In some regions, it has been shown that investments in water supply and sanitation can yield a net economic benefit, since the reductions in adverse health effects and health care costs outweigh the costs of undertaking the interventions. The drinkingwaterpotability.csv file contains water quality metrics for 3276 different water bodies. The columns in the file are as follows: ph, Hardness, Solids, Chloramines, Sulfate, Conductivity, Organic_carbon, Trihalomethanes, Turbidity, and Potability. Contaminated water and poor sanitation are linked to the transmission of diseases such as cholera, diarrhea, dysentery, hepatitis A, typhoid, and polio. Absent, inadequate, or inappropriately managed water and sanitation services expose individuals to preventable health risks. This is particularly the case in health care facilities where both patients and staff are placed at additional risk of infection and disease when water, sanitation, and hygiene services are lacking. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 2: DATA SCIENCE CRASH COURSE: Skin Cancer Classification and Prediction Using Machine Learning and Deep Learning Skin cancer develops primarily on areas of sun-exposed skin, including the scalp, face, lips, ears, neck, chest, arms and hands, and on the legs in women. But it can also form on areas that rarely see the light of day — your palms, beneath your fingernails or toenails, and your genital area. Skin cancer affects people of all skin tones, including those with darker complexions. When melanoma occurs in people with dark skin tones, it's more likely to occur in areas not normally exposed to the sun, such as the palms of the hands and soles of the feet. Dataset used in this project contains a balanced dataset of images of benign skin moles and malignant skin moles. The data consists of two folders with each 1800 pictures (224x244) of the two types of moles. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. The deep learning models used are CNN and MobileNet.
Java Deep Learning Projects
DOWNLOAD
Author : Md. Rezaul Karim
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-06-29
Java Deep Learning Projects written by Md. Rezaul Karim and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-29 with Computers categories.
Build and deploy powerful neural network models using the latest Java deep learning libraries Key Features Understand DL with Java by implementing real-world projects Master implementations of various ANN models and build your own DL systems Develop applications using NLP, image classification, RL, and GPU processing Book Description Java is one of the most widely used programming languages. With the rise of deep learning, it has become a popular choice of tool among data scientists and machine learning experts. Java Deep Learning Projects starts with an overview of deep learning concepts and then delves into advanced projects. You will see how to build several projects using different deep neural network architectures such as multilayer perceptrons, Deep Belief Networks, CNN, LSTM, and Factorization Machines. You will get acquainted with popular deep and machine learning libraries for Java such as Deeplearning4j, Spark ML, and RankSys and you’ll be able to use their features to build and deploy projects on distributed computing environments. You will then explore advanced domains such as transfer learning and deep reinforcement learning using the Java ecosystem, covering various real-world domains such as healthcare, NLP, image classification, and multimedia analytics with an easy-to-follow approach. Expert reviews and tips will follow every project to give you insights and hacks. By the end of this book, you will have stepped up your expertise when it comes to deep learning in Java, taking it beyond theory and be able to build your own advanced deep learning systems. What you will learn Master deep learning and neural network architectures Build real-life applications covering image classification, object detection, online trading, transfer learning, and multimedia analytics using DL4J and open-source APIs Train ML agents to learn from data using deep reinforcement learning Use factorization machines for advanced movie recommendations Train DL models on distributed GPUs for faster deep learning with Spark and DL4J Ease your learning experience through 69 FAQs Who this book is for If you are a data scientist, machine learning professional, or deep learning practitioner keen to expand your knowledge by delving into the practical aspects of deep learning with Java, then this book is what you need! Get ready to build advanced deep learning models to carry out complex numerical computations. Some basic understanding of machine learning concepts and a working knowledge of Java are required.
Data Science For Programmer A Project Based Approach With Python Gui
DOWNLOAD
Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2021-08-19
Data Science For Programmer A Project Based Approach With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-19 with Computers categories.
Book 1: Practical Data Science Programming for Medical Datasets Analysis and Prediction with Python GUI In this book, you will implement two data science projects using Scikit-Learn, Scipy, and other libraries with Python GUI. In Project 1, you will learn how to use Scikit-Learn, NumPy, Pandas, Seaborn, and other libraries to perform how to predict early stage diabetes using Early Stage Diabetes Risk Prediction Dataset provided by Kaggle. This dataset contains the sign and symptpom data of newly diabetic or would be diabetic patient. This has been collected using direct questionnaires from the patients of Sylhet Diabetes Hospital in Sylhet, Bangladesh and approved by a doctor. You will develop a GUI using PyQt5 to plot distribution of features, feature importance, cross validation score, and prediced values versus true values. The machine learning models used in this project are Adaboost, Random Forest, Gradient Boosting, Logistic Regression, and Support Vector Machine. In Project 2, you will learn how to use Scikit-Learn, NumPy, Pandas, and other libraries to perform how to analyze and predict breast cancer using Breast Cancer Prediction Dataset provided by Kaggle. Worldwide, breast cancer is the most common type of cancer in women and the second highest in terms of mortality rates.Diagnosis of breast cancer is performed when an abnormal lump is found (from self-examination or x-ray) or a tiny speck of calcium is seen (on an x-ray). After a suspicious lump is found, the doctor will conduct a diagnosis to determine whether it is cancerous and, if so, whether it has spread to other parts of the body. This breast cancer dataset was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg. You will develop a GUI using PyQt5 to plot distribution of features, pairwise relationship, test scores, prediced values versus true values, confusion matrix, and decision boundary. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, and Support Vector Machine. Book 2: Step by Step Tutorials For Data Science With Python GUI: Traffic And Heart Attack Analysis And Prediction In this book, you will implement two data science projects using Scikit-Learn, Scipy, and other libraries with Python GUI. In Chapter 1, you will learn how to use Scikit-Learn, Scipy, and other libraries to perform how to predict traffic (number of vehicles) in four different junctions using Traffic Prediction Dataset provided by Kaggle. This dataset contains 48.1k (48120) observations of the number of vehicles each hour in four different junctions: 1) DateTime; 2) Juction; 3) Vehicles; and 4) ID. In Chapter 2, you will learn how to use Scikit-Learn, NumPy, Pandas, and other libraries to perform how to analyze and predict heart attack using Heart Attack Analysis & Prediction Dataset provided by Kaggle. Book 3: BRAIN TUMOR: Analysis, Classification, and Detection Using Machine Learning and Deep Learning with Python GUI In this project, you will learn how to use Scikit-Learn, TensorFlow, Keras, NumPy, Pandas, Seaborn, and other libraries to implement brain tumor classification and detection with machine learning using Brain Tumor dataset provided by Kaggle. This dataset contains five first order features: Mean (the contribution of individual pixel intensity for the entire image), Variance (used to find how each pixel varies from the neighboring pixel 0, Standard Deviation (the deviation of measured Values or the data from its mean), Skewness (measures of symmetry), and Kurtosis (describes the peak of e.g. a frequency distribution). It also contains eight second order features: Contrast, Energy, ASM (Angular second moment), Entropy, Homogeneity, Dissimilarity, Correlation, and Coarseness. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, and Support Vector Machine. The deep learning models used in this project are MobileNet and ResNet50. In this project, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, training loss, and training accuracy.
Deep Learning With R Second Edition
DOWNLOAD
Author : Francois Chollet
language : en
Publisher: Simon and Schuster
Release Date : 2022-07-26
Deep Learning With R Second Edition written by Francois Chollet and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-26 with Computers categories.
Deep Learning with R, Second Edition shows you how to put deep learning into action. It’s based on the revised new edition of François Chollet’s bestselling Deep Learning with Python. All code and examples have been expertly translated to the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages at RStudio. Novices and experienced ML practitioners will love the expert insights, practical techniques, and important theory for building neural networks.
Deep Learning With Python Second Edition
DOWNLOAD
Author : Francois Chollet
language : en
Publisher: Simon and Schuster
Release Date : 2021-12-21
Deep Learning With Python Second Edition written by Francois Chollet and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-21 with Computers categories.
Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. You'll learn directly from the creator of Keras, François Chollet, building your understanding through intuitive explanations and practical examples. Updated from the original bestseller with over 50% new content, this second edition includes new chapters, cutting-edge innovations, and coverage of the very latest deep learning tools. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.
Proceedings Of 7th International Conference On Civil Engineering And Architecture Volume 1
DOWNLOAD
Author : Thomas Kang
language : en
Publisher: Springer Nature
Release Date : 2025-05-02
Proceedings Of 7th International Conference On Civil Engineering And Architecture Volume 1 written by Thomas Kang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-02 with Technology & Engineering categories.
This book states that the proceedings gathers selected papers from 7th International Conference on Civil Engineering and Architecture (ICCEA 2024), which was held in Da Nang, Vietnam on December 7-9, 2024. The conference is the premier forum for the presentation of new advances and research results in the fields of theoretical, experimental, and practical civil engineering and architecture. And this proceedings from the conference mainly discusses architectural design and project management, environmental protection and spatial planning, design and analysis of building materials, and structural engineering and safety. And these materials can be useful and valuable sources for researchers and professionals working in the field of civil engineering and architecture.
Digital Transformation In The Construction Industry
DOWNLOAD
Author : Ehsan Noroozinejad Farsangi
language : en
Publisher: Woodhead Publishing
Release Date : 2025-05-16
Digital Transformation In The Construction Industry written by Ehsan Noroozinejad Farsangi and has been published by Woodhead Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-16 with Technology & Engineering categories.
Digital Transformation in the Construction Industry: Sustainability, Resilience, and Data-Centric Engineering delivers timely and much sought-after guidance related to novel, digital-first practices and the latest technological tools, the gradual adoption of which is being embraced to significantly reshape the way buildings and other infrastructure assets are designed, constructed, operated, and maintained.Methodological and practice-informed investigations by scholars and researchers from across the globe, providing a wealth of knowledge relevant for, and applicable to, different geographical and economic contexts, are coherently collated in this edited volume. This systematic analysis of cutting-edge developments (such as Building Information Modeling, Internet of Things, Artificial Intelligence, Machine Learning, Big Data, Augmented Reality, Virtual Reality, 3D Printing, and Structural Health Monitoring) is accompanied by discussions on challenges and opportunities that digitalization engenders. Additionally, real-word case studies enrich the coverage, highlighting how these innovative solutions can contribute to establishing working efficiencies that can at the same time aid the impactful realization of globally recognized sustainability goals.Readers in both academic and professional settings are, therefore, not only equipped with a comprehensive overview of the state of the art but also offered an insightful reference resource for future works in the area. - Covers emerging technologies comprehensively - Emphasizes the use of digital tools to support achievements for worldwide net zero targets - Focuses on lean and agile construction practices to improve project efficiency and reduce waste
Proceedings Of Fourth International Conference On Computer And Communication Technologies
DOWNLOAD
Author : K. Ashoka Reddy
language : en
Publisher: Springer Nature
Release Date : 2023-03-29
Proceedings Of Fourth International Conference On Computer And Communication Technologies written by K. Ashoka Reddy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-29 with Technology & Engineering categories.
The book is a compilation of high-quality scientific papers presented at the 4th International Conference on Computer & Communication Technologies (IC3T 2022). The book covers cutting-edge technologies and applications of soft computing, artificial intelligence and communication. In addition, a variety of further topics are discussed, which include data mining, machine intelligence, fuzzy computing, sensor networks, signal and image processing, human-computer interaction, and web intelligence.
Deep Learning For Toxicity And Disease Prediction
DOWNLOAD
Author : Ping Gong
language : en
Publisher: Frontiers Media SA
Release Date : 2020-04-01
Deep Learning For Toxicity And Disease Prediction written by Ping Gong and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-01 with categories.
Neural Network Projects With Python
DOWNLOAD
Author : James Loy
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-02-28
Neural Network Projects With Python written by James Loy and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-28 with Computers categories.
Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python Key FeaturesDiscover neural network architectures (like CNN and LSTM) that are driving recent advancements in AIBuild expert neural networks in Python using popular libraries such as KerasIncludes projects such as object detection, face identification, sentiment analysis, and moreBook Description Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them. It contains practical demonstrations of neural networks in domains such as fare prediction, image classification, sentiment analysis, and more. In each case, the book provides a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and the associated Python code to implement the solution from scratch. In the process, you will gain hands-on experience with using popular Python libraries such as Keras to build and train your own neural networks from scratch. By the end of this book, you will have mastered the different neural network architectures and created cutting-edge AI projects in Python that will immediately strengthen your machine learning portfolio. What you will learnLearn various neural network architectures and its advancements in AIMaster deep learning in Python by building and training neural networkMaster neural networks for regression and classificationDiscover convolutional neural networks for image recognitionLearn sentiment analysis on textual data using Long Short-Term MemoryBuild and train a highly accurate facial recognition security systemWho this book is for This book is a perfect match for data scientists, machine learning engineers, and deep learning enthusiasts who wish to create practical neural network projects in Python. Readers should already have some basic knowledge of machine learning and neural networks.