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Titlebook: Implementing Machine Learning for Finance; A Systematic Approac Tshepo Chris Nokeri Book 2021 Tshepo Chris Nokeri 2021 Machine Learning.Dee

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樓主: Hoover
31#
發(fā)表于 2025-3-27 00:01:14 | 只看該作者
32#
發(fā)表于 2025-3-27 03:29:32 | 只看該作者
Market Trend Classification Using ML and DL,riable with over two outcomes. In this chapter, we use both SciKit-Learn and Keras. The SciKit-Learn library is pre-installed in the Python environment. To install Keras in the Python environment, we use ., and in the conda environment, we use ..
33#
發(fā)表于 2025-3-27 07:52:59 | 只看該作者
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發(fā)表于 2025-3-27 09:52:00 | 只看該作者
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發(fā)表于 2025-3-27 14:34:54 | 只看該作者
Forecasting Using ARIMA, SARIMA, and the Additive Model,ssive integrated moving average (ARIMA) model, seasonal ARIMA (SARIMA) model, and additive model, to identify patterns in currency pairs and forecast future prices. In this chapter, we use . to scrape financial data from Yahoo Finance, and we use .. For time-series modeling, we use the . library, wh
36#
發(fā)表于 2025-3-27 21:49:58 | 只看該作者
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發(fā)表于 2025-3-28 00:28:15 | 只看該作者
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發(fā)表于 2025-3-28 03:20:27 | 只看該作者
Tshepo Chris Nokerint of biases. The impact of human visualization literacy, competence and human cognition on cognitive biases are also examined, as well as the notion of system-induced biases. The well referenced chapters provide an excellent starting point for gaining an awareness of the detrimental effect that som
39#
發(fā)表于 2025-3-28 09:12:26 | 只看該作者
Tshepo Chris Nokerigoal of designing information visualization tools to support the process. We list examples of potential reviewer biases identified and present theoretical ideas on how the biases can be mitigated through visualization tools. These ideas include employing strategies that conflict with the conventiona
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發(fā)表于 2025-3-28 14:13:34 | 只看該作者
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