Deep Learning for Time Series Forecasting: Predict the...

Main
Computers - Programming
Deep Learning for Time Series...

Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python

Jason Brownlee

5.0 / 5.0

¿Qué tanto le ha gustado este libro?

¿De qué calidad es el archivo descargado?

Descargue el libro para evaluar su calidad

¿Cuál es la calidad de los archivos descargados?

Categorías:

Computers - Programming

Año:

2019

Edición:

v1.6

Editorial:

Machine Learning Mastery

Idioma:

english

Páginas:

572

Archivo:

PDF, 8.27 MB

IPFS:

,

english, 2019

Leer en línea

Conversión a en curso

La conversión a ha fallado

También le puede interesar

Jason Brownlee — Probability for Machine Learning - Discover How To Harness Uncertainty With Python

Jason Brownlee — Better Deep Learning: Train Faster, Reduce Overfitting, and Make Better Predictions

Jason Brownlee — Data Preparation for Machine Learning - Data Cleaning, Feature Selection, and Data Transforms in Python

Jason Brownlee — Deep Learning for Computer Vision - Image Classification, Object Detection and Face Recognition in Python

Jason Brownlee — Introduction to Time Series Forecasting with Python: How to Prepare Data and Develop Models to Predict the Future (Version 1.9)

Jason Brownlee — Machine learning mastery with Weka

New Era — Time Series Forecasting using Python

Jason Brownlee — Ensemble Machine Learning With Python: 7-Day Mini-Course

LazyProgrammer — Convolutional Neural Networks in Python: Master Data Science and Machine Learning with Modern Deep Learning in Python, Theano, and TensorFlow (Machine Learning in Python)

LazyProgrammer — Deep Learning: Recurrent Neural Networks in Python: LSTM, GRU, and more RNN machine learning architectures in Python and Theano (Machine Learning in Python)

Jason Brownlee — Machine Learning Mastery with R: Get Started, Build Accurate Models and Work Through Projects Step-by-Step

Adrian Rosebrock — Deep Learning for Computer Vision with Python, Volume 2

Adrian Rosebrock — Deep Learning for Computer Vision with Python, Volume 3

Jason Brownlee — Deep Learning with Python: Develop Deep Learning Models on Theano and TensorFLow Using Keras

Jason Brownlee — Machine Learning Mastery with Python: Understand Your Data, Create Accurate Models and Work Projects End-To-End

Jason Brownlee — Statistical Methods for Machine Learning: Discover How to Transform Data into Knowledge with Python

Jason Brownlee — Generative Adversarial Networks with Python: Deep Learning Generative Models for Image Synthesis and Image Translation

Jason Brownlee — Deep Learning for Time Series Forecasting - Predict the Future with MLPs, CNNs and LSTMs in Python

Jason Brownlee — Long Short-Term Memory Networks With Python: Develop Sequence Prediction Models With Deep Learning

Jason Brownlee — Data Preparation for Machine Learning: Data Cleaning, Feature Selection, and Data Transforms in Python

Josh Hugh Learning — Python Machine Learning. A Crash Course for Beginners to Understand Machine learning, Artificial Intelligence, Neural Networks, and Deep Learning with Scikit-Learn, TensorFlow, and Keras.

Jason Brownlee — Deep Learning for Natural Language Processing: Develop Deep Learning Models for Natural Language in Python

Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal — Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition

Eugene Charniak — Introduction to Deep Learning

Ruey S. Tsay — Analysis of Financial Time Series, Third Edition (Wiley Series in Probability and Statistics)

Andrew R. Webb — Statistical pattern recognition

Galit Shmueli, Kenneth C. Lichtendahl Jr — Practical Time Series Forecasting with R: A Hands-On Guide, 2nd Edition

Marc S. Paolella — Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH

Francesca Lazzeri — Machine Learning for Time Series Forecasting with Python

Jason Brownlee — Clever Algorithms. Nature-Inspired Programming Recipes

Jason Brownlee — Machine Learning Mastery with R

J. Patterson, A. Gibson — Deep Learning: A Practitioner's Approach

Alice Zheng, Amanda Casari — Feature Engineering for Machine Learning

Tom Hope, Yehezkel S. Resheff, Itay Lieder — Learning TensorFlow

Anirudh Koul, Siddha Ganju, Meher Kasam — Practical Deep Learning for Cloud and Mobile

Seth Weidman — Deep Learning from Scratch

Aileen Nielsen — Practical Time Series Analysis: Prediction with Statistics and Machine Learning

David Foster — Generative Deep Learning

Ian Pointer — Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications

Sylvain Gugger, Jeremy Howard — Deep Learning for Coders with Fastai and PyTorch

N D Lewis — Deep Time Series Forecasting with Python: An Intuitive Introduction to Deep Learning for Applied Time Series Modeling

Francois Chollet — Deep Learning with Python, 2nd Edition (Final Release)

Términos más frecuentes

Listas de libros relacionados