(從我的一個喜歡與尊敬的作家,賽斯 高汀)
這就是它傾斜之前的那一瞬間,只要稍微多付出一點努力就能發揮重大的作用。
我們時常都在等著這一刻。在參與的那一天得到真正的回報,為我們的努力帶來最大的影響與機械化的優勢。
但是這是一個神話。
我們的最大的槓桿作用是能做出承諾,用每日的持久性,漸進性,積極性,以及表面上不太費力所做出的成果。
當那槓桿作用起效時,它看起來像我們能隨時隨地把握住好的時機。
"Professionals do work and ship art." That is my aspiration!
(從我的一個喜歡與尊敬的作家,賽斯 高汀)
這就是它傾斜之前的那一瞬間,只要稍微多付出一點努力就能發揮重大的作用。
我們時常都在等著這一刻。在參與的那一天得到真正的回報,為我們的努力帶來最大的影響與機械化的優勢。
但是這是一個神話。
我們的最大的槓桿作用是能做出承諾,用每日的持久性,漸進性,積極性,以及表面上不太費力所做出的成果。
當那槓桿作用起效時,它看起來像我們能隨時隨地把握住好的時機。
In the book, Bare Bones Change Management: What you shouldn’t not do, Bob Lewis explained the seven must-have elements for any change management effort to have a chance of succeeding. Here are my takeaways from one of the topics discussed in the book.
Bob outlined some tactics to consider when working with the cycle time and throughput constraints.
Improved cycle time is about increasing customer satisfaction while improving quality. Lower cycle time brings about lower fixed costs because fewer assets are tied up by the cycles.
The common trade-off for improved cycle time is reduced throughput.
Organizations can consider the following options to improve cycle time.
Improved throughput is about increasing volume with the same unit of time. Higher throughput brings about scalability and lower incremental costs.
The common trade-off for improved throughput is longer cycle time.
Organizations can consider the following options to improve throughput.
Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.
Dataset Used: Bike Sharing Dataset
Dataset ML Model: Regression with numerical attributes
Dataset Reference: https://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset
For available performance benchmarks, please consult: https://www.kaggle.com/contactprad/bike-share-daily-data
INTRODUCTION: Using the data generated by a bike sharing system, this project attempts to predict the daily demand for bike sharing. For this iteration (Take No.2) of the project, we attempt to use the data available, transform as necessary, and apply the Stochastic Gradient Boosting algorithm to examine the modeling effectiveness. Again, the goal of this iteration is to examine various data transformation options and find a sufficiently accurate (low error) combination for future prediction tasks.
This iteration of the project will test the following four modeling scenarios:
Scenario No.1: Remove the attribute “atemp” since it was highly correlated with the attribute “temp.”
Scenario No.2: Perform one-hot-encoding on the variable “season.”
Scenario No.3: Perform one-hot-encoding on the variable “mnth.”
For scenarios 2-3, steps from section No.3 and No.4 will be repeated for each scenario.
CONCLUSION: The baseline performance of the Stochastic Gradient Boosting stands at an RMSE value of 1240 and an R-square value of 0.5943 using the training data. Scenario No.1 did slightly better with an RMSE value of 1233 and an R-square value of 0.5991. As the result, we will leverage scenario No.1 to training the final model and observe how it will do with the validation dataset.
The final Stochastic Gradient Boosting model processed the validation dataset with an RMSE value of 1180 and an R-square value of 0.6320, which was slightly worse than the Take No.1 result of 1177 for RMSE and 0.6329 for R-square. For this iteration of the project, data transformation did not improve the model performance with a noticeable outcome.
The HTML formatted report can be found here on GitHub.
(從我的一個喜歡與尊敬的作家,賽斯 高汀)
正確的答案可能不是你能給的最好的建議。
如果你能幫助你的朋友採取行動,那也許會更好。找尋與去做的行為通常總是會比獲得好的建議來更有用。
煽動行動往往會比提供洞察力來的更好。要更好地繼續前進,找出解決問題的辦法,而不是停下來因為你相信你知道最正確的答案。
In the podcast series, Seth Godin’s Startup School, Seth Godin gave a guided tour to a group of highly-motivated early-stage entrepreneurs on some of the questions they will have to dig deep and ask themselves while they build up their business. Here are my takeaways from various topics discussed in the podcast episodes.
In his podcast, Akimbo, Seth Godin teaches us how to adopt a posture of possibility, change the culture, and choose to make a difference. Here are my takeaways from the episode.
Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.
Dataset Used: Bike Sharing Dataset
Dataset ML Model: Regression with numerical attributes
Dataset Reference: https://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset
For available performance benchmarks, please consult: https://www.kaggle.com/contactprad/bike-share-daily-data
INTRODUCTION: Using the data generated by a bike sharing system, this project attempts to predict the daily demand for bike sharing. For this iteration of the project, we attempt to use the data available for discovering a suitable machine learning algorithm that future predictions can use. We have kept the data transformation activities to a minimum and drop the several attributes that do not make sense to keep or simply will not help in training the model. Again, the goal of this iteration is to find a sufficiently accurate (low error) algorithm for the future prediction tasks.
CONCLUSION: The baseline performance of predicting the target variable achieved an average RMSE value of 1322. Three algorithms (Bagged CART, Random Forest, and Stochastic Gradient Boosting) achieved the lower RMSE and higher R-square values during the initial modeling round. After a series of tuning trials with these three algorithms, Stochastic Gradient Boosting produced the lowest RMSE value of 1213 and the highest R-square value at 0.6093 using the training data.
Stochastic Gradient Boosting also processed the validation dataset with an RMSE value of 1177 and an R-square value of 0.6329, which was better than the average training result. For this project, the Stochastic Gradient Boosting ensemble algorithm yielded consistently top-notch training and validation results, which warrant the additional processing required by the algorithm.
The HTML formatted report can be found here on GitHub.
(從我的一個喜歡與尊敬的作家,賽斯 高汀)
你不必去申請。你不會得到工資。也沒有人會來挑選你。
當你大肆吹噓的你籌集了多少錢,或者你的估值是多少,那是一種辦公的思維。
創業是做出一個將解決的方案給一個有問題需要解決的人來做的交易機會。
能解決更多問題,能解決更大的問題,能更廣泛地解決問題,那你就是企業家。
將一項工作實現工業化,使之與規則,老闆和流程相適應是很有誘惑力的。但這不是創業的核心。
創業是需要以你感到自豪的方式來解決問題。
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