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 https://www.automl.org/automl/ AUTOML What is AutoML? Automated Machine Learning provides methods and processes to make Machine Learning available for non-Machine Learning experts, to improve efficiency of Machine Learning and to accelerate research on Machine Learning. Machine learning (ML) has achieved considerable successes in recent years and an ever-growing number of disciplines rely on it. However, this success crucially relies on human machine learning experts to perform the following tasks: Preprocess and clean the data. Select and construct appropriate features. Select an appropriate model family. Optimize model hyperparameters. Postprocess machine learning models. Critically analyze the results obtained. As the complexity of these tasks is often beyond non-ML-experts, the rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. We call the resulting research area that t

https://keras.io/api/layers/recurrent_layers/lstm/ https://buomsoo-kim.github.io/keras/2019/07/12/Easy-deep-learning-with-Keras-19.md/ Long Short-Term Memory layer - Hochreiter 1997. See  the Keras RNN API guide  for details about the usage of RNN API. Based on available runtime hardware and constraints, this layer will choose different implementations (cuDNN-based or pure-TensorFlow) to maximize the performance. If a GPU is available and all the arguments to the layer meet the requirement of the CuDNN kernel (see below for details), the layer will use a fast cuDNN implementation. The requirements to use the cuDNN implementation are: activation  ==  tanh recurrent_activation  ==  sigmoid recurrent_dropout  == 0 unroll  is  False use_bias  is  True Inputs, if use masking, are strictly right-padded. Eager execution is enabled in the outermost context. For example: >>> inputs = tf . random . normal ( [ 32 , 10 , 8 ] ) >>> lstm = tf . keras . layers . LSTM ( 4 ) &

https://www.machinelearningplus.com/time-series/arima-model-time-series-forecasting-python/#:~:text=ARIMA%2C%20short%20for%20'Auto%20Regressive,used%20to%20forecast%20future%20values.  ARIMA Model – Complete Guide to Time Series Forecasting in Python by  Selva Prabhakaran   | Posted on Facebook Twitter WhatsApp LinkedIn Reddit Google Bookmarks 나누기 Using ARIMA model, you can forecast a time series using the series past values. In this post, we build an optimal ARIMA model from scratch and extend it to Seasonal ARIMA (SARIMA) and SARIMAX models. You will also see how to build autoarima models in python ARIMA Model – Time Series Forecasting. Photo by Cerquiera Contents Introduction to Time Series Forecasting Introduction to ARIMA Models What does the p, d and q in ARIMA model mean? What are AR and MA models How to find the order of differencing (d) in ARIMA model How to find the order of the AR term (p) How to find the order of the MA term (q) How to handle if a time series is slightl