دسترسی نامحدود
برای کاربرانی که ثبت نام کرده اند
دانلود کتاب Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python
دانلود کتاب یادگیری عمیق برای پیش‌بینی سری‌های زمانی: آینده را با MLP، CNN و LSTM در پایتون پیش‌بینی کنید
مشخصات کتاب
Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python
دسته بندی: برنامه نويسي ویرایش: 1.6 نویسندگان: Jason Brownlee سری: Machine Learning Mastery ناشر: Independently Published سال نشر: 2019 تعداد صفحات: 572 زبان: English فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) حجم فایل: 8 مگابایت
قیمت کتاب (تومان) : 37,000
در صورت تبدیل فایل کتاب Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب یادگیری عمیق برای پیش‌بینی سری‌های زمانی: آینده را با MLP، CNN و LSTM در پایتون پیش‌بینی کنید نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
توضیحاتی در مورد کتاب یادگیری عمیق برای پیش‌بینی سری‌های زمانی: آینده را با MLP، CNN و LSTM در پایتون پیش‌بینی کنید
روش‌های یادگیری عمیق برای پیش‌بینی سری‌های زمانی بسیار نوید می‌دهند، مانند یادگیری خودکار وابستگی زمانی و مدیریت خودکار ساختارهای زمانی مانند روندها و فصلی. در این کتاب الکترونیکی جدید که به سبک دوستانه تسلط یادگیری ماشینی که به آن عادت کرده‌اید نوشته شده است، از ریاضیات صرف نظر کرده و مستقیماً به نتایج برسید. با توضیحات واضح، کتابخانه‌های استاندارد پایتون (Keras و TensorFlow 2)، و درس‌های آموزشی گام به گام، خواهید فهمید که چگونه مدل‌های یادگیری عمیق را برای پروژه‌های پیش‌بینی سری زمانی خود توسعه دهید.
توضیحاتی درمورد کتاب به خارجی
Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality. In this new Ebook written in the friendly Machine Learning Mastery style that you’re used to, skip the math and jump straight to getting results. With clear explanations, standard Python libraries (Keras and TensorFlow 2), and step-by-step tutorial lessons you’ll discover how to develop deep learning models for your own time series forecasting projects.
فهرست مطالب
Copyright Contents Preface I Introduction II Foundations Promise of Deep Learning for Time Series Forecasting Time Series Forecasting Multilayer Perceptrons for Time Series Convolutional Neural Networks for Time Series Recurrent Neural Networks for Time Series Promise of Deep Learning Extensions Further Reading Summary Taxonomy of Time Series Forecasting Problems Framework Overview Inputs vs. Outputs Endogenous vs. Exogenous Regression vs. Classification Unstructured vs. Structured Univariate vs. Multivariate Single-step vs. Multi-step Static vs. Dynamic Contiguous vs. Discontiguous Framework Review Extensions Further Reading Summary How to Develop a Skillful Forecasting Model The Situation Process Overview How to Use This Process Step 1: Define Problem Step 2: Design Test Harness Step 3: Test Models Step 4: Finalize Model Extensions Further Reading Summary How to Transform Time Series to a Supervised Learning Problem Supervised Machine Learning Sliding Window Sliding Window With Multiple Variates Sliding Window With Multiple Steps Implementing Data Preparation Extensions Further Reading Summary Review of Simple and Classical Forecasting Methods Simple Forecasting Methods Autoregressive Methods Exponential Smoothing Methods Extensions Further Reading Summary III Deep Learning Methods How to Prepare Time Series Data for CNNs and LSTMs Overview Time Series to Supervised 3D Data Preparation Basics Data Preparation Example Extensions Further Reading Summary How to Develop MLPs for Time Series Forecasting Tutorial Overview Univariate MLP Models Multivariate MLP Models Multi-step MLP Models Multivariate Multi-step MLP Models Extensions Further Reading Summary How to Develop CNNs for Time Series Forecasting Tutorial Overview Univariate CNN Models Multivariate CNN Models Multi-step CNN Models Multivariate Multi-step CNN Models Extensions Further Reading Summary How to Develop LSTMs for Time Series Forecasting Tutorial Overview Univariate LSTM Models Multivariate LSTM Models Multi-step LSTM Models Multivariate Multi-step LSTM Models Extensions Further Reading Summary IV Univariate Forecasting Review of Top Methods For Univariate Time Series Forecasting Overview Study Motivation Time Series Datasets Time Series Forecasting Methods Data Preparation One-step Forecasting Results Multi-step Forecasting Results Outcomes Extensions Further Reading Summary How to Develop Simple Methods for Univariate Forecasting Tutorial Overview Simple Forecasting Strategies Develop a Grid Search Framework Case Study 1: No Trend or Seasonality Case Study 2: Trend Case Study 3: Seasonality Case Study 4: Trend and Seasonality Extensions Further Reading Summary How to Develop ETS Models for Univariate Forecasting Tutorial Overview Develop a Grid Search Framework Case Study 1: No Trend or Seasonality Case Study 2: Trend Case Study 3: Seasonality Case Study 4: Trend and Seasonality Extensions Further Reading Summary How to Develop SARIMA Models for Univariate Forecasting Tutorial Overview Develop a Grid Search Framework Case Study 1: No Trend or Seasonality Case Study 2: Trend Case Study 3: Seasonality Case Study 4: Trend and Seasonality Extensions Further Reading Summary How to Develop MLPs, CNNs and LSTMs for Univariate Forecasting Tutorial Overview Time Series Problem Model Evaluation Test Harness Multilayer Perceptron Model Convolutional Neural Network Model Recurrent Neural Network Models Extensions Further Reading Summary How to Grid Search Deep Learning Models for Univariate Forecasting Tutorial Overview Time Series Problem Develop a Grid Search Framework Multilayer Perceptron Model Convolutional Neural Network Model Long Short-Term Memory Network Model Extensions Further Reading Summary V Multi-step Forecasting How to Load and Explore Household Energy Usage Data Tutorial Overview Household Power Consumption Dataset Load Dataset Patterns in Observations Over Time Time Series Data Distributions Ideas on Modeling Extensions Further Reading Summary How to Develop Naive Models for Multi-step Energy Usage Forecasting Tutorial Overview Problem Description Load and Prepare Dataset Model Evaluation Develop Naive Forecast Models Extensions Further Reading Summary How to Develop ARIMA Models for Multi-step Energy Usage Forecasting Tutorial Overview Problem Description Load and Prepare Dataset Model Evaluation Autocorrelation Analysis Develop an Autoregressive Model Extensions Further Reading Summary How to Develop CNNs for Multi-step Energy Usage Forecasting Tutorial Overview Problem Description Load and Prepare Dataset Model Evaluation CNNs for Multi-step Forecasting Univariate CNN Model Multi-channel CNN Model Multi-headed CNN Model Extensions Further Reading Summary How to Develop LSTMs for Multi-step Energy Usage Forecasting Tutorial Overview Problem Description Load and Prepare Dataset Model Evaluation LSTMs for Multi-step Forecasting Univariate Input and Vector Output Encoder-Decoder LSTM With Univariate Input Encoder-Decoder LSTM With Multivariate Input CNN-LSTM Encoder-Decoder With Univariate Input ConvLSTM Encoder-Decoder With Univariate Input Extensions Further Reading Summary VI Time Series Classification Review of Deep Learning Models for Human Activity Recognition Overview Human Activity Recognition Benefits of Neural Network Modeling Supervised Learning Data Representation Convolutional Neural Network Models Recurrent Neural Network Models Extensions Further Reading Summary How to Load and Explore Human Activity Data Tutorial Overview Activity Recognition Using Smartphones Dataset Download the Dataset Load the Dataset Balance of Activity Classes Plot Time Series Per Subject Plot Distribution Per Subject Plot Distribution Per Activity Plot Distribution of Activity Duration Approach to Modeling Model Evaluation Extensions Further Reading Summary How to Develop ML Models for Human Activity Recognition Tutorial Overview Activity Recognition Using Smartphones Dataset Modeling Feature Engineered Data Modeling Raw Data Extensions Further Reading Summary How to Develop CNNs for Human Activity Recognition Tutorial Overview Activity Recognition Using Smartphones Dataset CNN for Activity Recognition Tuned CNN Model Multi-headed CNN Model Extensions Further Reading Summary How to Develop LSTMs for Human Activity Recognition Tutorial Overview Activity Recognition Using Smartphones Dataset LSTM Model CNN-LSTM Model ConvLSTM Model Extensions Further Reading Summary VII Appendix Getting Help Applied Time Series Official Keras Destinations Where to Get Help with Keras Time Series Datasets How to Ask Questions Contact the Author How to Setup a Workstation for Python Overview Download Anaconda Install Anaconda Start and Update Anaconda Install Deep Learning Libraries Further Reading Summary VIII Conclusions How Far You Have Come
نظرات کاربران
کتاب های مرتبط
دانلود کتاب UML Java Reverse Engineering Tutorial - Windows Platform
دانلود کتاب Professional Scrum Development with Azure DevOps
دانلود کتاب FM 350-2 Counter Function Module. Installation and Parameter Assignment
دانلود کتاب Applied WPF 4 in Context
دانلود کتاب C++ Standard Library Quick Reference
دانلود کتاب 计算几何 : 算法设计与分析
دانلود کتاب Zbiór zadań z programowania w języku C/C++
