我們應該如何慶祝你的節日?

(從我一個尊敬的作家,賽斯·高汀

如果今天是紀念你的節日,那主題會是什麼?

如果我們必須檢查關於你的一切,你的業績,你的影響力,你的聲譽,我們會畫出一個什麼樣的正面畫? 我們會用什麼樣的口號,橫幅和問候語來慶祝您和您的工作?

將一個組織或一個人的工作歸結為一兩句話是不完全准確的,但無論如何我們都會這樣做。

你的歸結又是什麼呢?

Multi-Class Tabular Classification Model for Durum Wheat Identification Using Python and TensorFlow

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Durum Wheat Identification dataset is a multi-class modeling situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: Wheat is the main ingredient of most common food products in many people’s daily lives. Obtaining good quality wheat kernels is an essential matter for food supplies. In this study, the research team attempted to examine and classify type-1252 durum wheat kernels to obtain top-quality crops based on their vitreousness. The researchers used a total of 236 morphological, color, wavelet, and gaborlet features to classify durum wheat kernels and foreign objects by training several Artificial Neural Networks (ANNs) with different amounts of elements based on the feature rank list obtained with the ANOVA test.

ANALYSIS: The average performance of the preliminary TensorFlow models achieved an accuracy benchmark of 99.23%. When we processed the test dataset with the final model, the model achieved an accuracy score of 99.55%.

CONCLUSION: In this iteration, TensorFlow appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Durum Wheat Dataset

Dataset ML Model: Multi-Class classification with numerical features

Dataset Reference: https://www.muratkoklu.com/datasets/

One source of potential performance benchmarks: https://doi.org/10.1016/j.compag.2019.105016

The HTML formatted report can be found here on GitHub.

Multi-Class Tabular Classification Model for Durum Wheat Identification Using Python and XGBoost

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Durum Wheat Identification dataset is a multi-class modeling situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: Wheat is the main ingredient of most common food products in many people’s daily lives. Obtaining good quality wheat kernels is an essential matter for food supplies. In this study, the research team attempted to examine and classify type-1252 durum wheat kernels to obtain top-quality crops based on their vitreousness. The researchers used a total of 236 morphological, color, wavelet, and gaborlet features to classify durum wheat kernels and foreign objects by training several Artificial Neural Networks (ANNs) with different amounts of elements based on the feature rank list obtained with the ANOVA test.

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 99.30%. After a series of tuning trials, the final model processed the training dataset with an accuracy score of 99.60%. When we processed the test dataset with the final model, the model achieved an accuracy score of 99.55%.

CONCLUSION: In this iteration, the XGBoost model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Durum Wheat Dataset

Dataset ML Model: Multi-Class classification with numerical features

Dataset Reference: https://www.muratkoklu.com/datasets/

One source of potential performance benchmarks: https://doi.org/10.1016/j.compag.2019.105016

The HTML formatted report can be found here on GitHub.

Univariate Time Series Model for Annual Immigration into USA Using TensorFlow

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The project aims to construct a time series prediction model and document the end-to-end steps using a template. The Annual Immigration into USA dataset is a univariate time series situation where we attempt to forecast future outcomes based on past data points.

INTRODUCTION: The problem is to forecast the annual number of people immigrating to the United States. The dataset describes a time-series of people (in thousands) over 143 years (1820-1962), and there are 143 observations. We used the first 80% of the observations for training and testing various models while holding back the remaining observations for validating the final model.

ANALYSIS: The baseline persistence model yielded an RMSE of 52,116. The LSTM model processed the same test data with an RMSE of 38,031, which was better than the baseline model as expected. In an earlier ARIMA modeling experiment, the best ARIMA model with non-seasonal order of (0, 1, 2) processed the validation data with an RMSE of 61,789.

CONCLUSION: For this dataset, the TensorFlow LSTM model achieved an acceptable result, and we should consider using TensorFlow for further modeling.

Dataset Used: Annual immigration into the United States, 1820-1962.

Dataset ML Model: Time series forecast with numerical attribute.

Dataset Reference: Rob Hyndman and Yangzhuoran Yang (2018). tsdl: Time Series Data Library. v0.1.0. https://pkg.yangzhuoranyang./tsdl/.

The HTML formatted report can be found here on GitHub.

Multi-Class Tabular Classification Model for Durum Wheat Identification Using Python and TensorFlow Decision Forests

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Durum Wheat Identification dataset is a multi-class modeling situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: Wheat is the main ingredient of most common food products in many people’s daily lives. Obtaining good quality wheat kernels is an essential matter for food supplies. In this study, the research team attempted to examine and classify type-1252 durum wheat kernels to obtain top-quality crops based on their vitreousness. The researchers used a total of 236 morphological, color, wavelet, and gaborlet features to classify durum wheat kernels and foreign objects by training several Artificial Neural Networks (ANNs) with different amounts of elements based on the feature rank list obtained with the ANOVA test.

ANALYSIS: The performance of the preliminary Gradient Boosted Trees model achieved an accuracy benchmark of 99.97% on the training dataset. When we applied the finalized model to Kaggle’s test dataset, the model achieved an accuracy score of 99.88%.

CONCLUSION: In this iteration, the TensorFlow Decision Forests model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Durum Wheat Dataset

Dataset ML Model: Multi-Class classification with numerical features

Dataset Reference: https://www.muratkoklu.com/datasets/

One source of potential performance benchmarks: https://doi.org/10.1016/j.compag.2019.105016

The HTML formatted report can be found here on GitHub.

Multi-Class Tabular Classification Model for Durum Wheat Identification Using Python and Scikit-Learn

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Durum Wheat Identification dataset is a multi-class modeling situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: Wheat is the main ingredient of most common food products in many people’s daily lives. Obtaining good quality wheat kernels is an essential matter for food supplies. In this study, the research team attempted to examine and classify type-1252 durum wheat kernels to obtain top-quality crops based on their vitreousness. The researchers used a total of 236 morphological, color, wavelet, and gaborlet features to classify durum wheat kernels and foreign objects by training several Artificial Neural Networks (ANNs) with different amounts of elements based on the feature rank list obtained with the ANOVA test.

ANALYSIS: The average performance of the machine learning algorithms achieved an accuracy benchmark of 98.65% using the training dataset. Furthermore, we selected k-Nearest Neighbors as the final model as it processed the training dataset with a final accuracy score of 99.56%. When we processed the test dataset with the final model, the model achieved an accuracy score of 99.66%.

CONCLUSION: In this iteration, the k-Nearest Neighbors model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Durum Wheat Dataset

Dataset ML Model: Multi-Class classification with numerical features

Dataset Reference: https://www.muratkoklu.com/datasets/

One source of potential performance benchmarks: https://doi.org/10.1016/j.compag.2019.105016

The HTML formatted report can be found here on GitHub.

Roz Zander and Ben Zander on The Art of Possibility, Part 8

In the book, The Art of Possibility: Transforming Professional and Personal Life, Rosamund Stone Zander and Benjamin Zander show us the 12 things we can do to go on a journey of possibility, rather than living a life full of hurdles and constraints of our own making.

These are some of my favorite concepts and takeaways from reading the book.

The Eighth Practice: Giving Way to Passion

In this chapter, Roz and Ben discuss what it means to give way to passion. They offer the following observations and recommendations for us to think about:

In the universe of possibility, vibrancy and energy surround us. Yet, while the universe is sparking with generative power, how do we tap into the source? Where can we find an electric socket for vitality?

One way to find the energy is to recognize that vital, expressive energy flows everywhere. Such energy is the medium for the existence of life. The choice of participating in that vitality lies within ourselves.

Very often, our consciousness tells us a different story. The world comes to us with numerous boundaries and constraints. Culture has taught us to strictly operate within limits, stay in our lanes, and not compare apples and oranges. Yet our minds and bodies are perfectly capable of suspending an edge once we know how and where the lines are drawn.

Roz and Ben suggested that we take the following two steps to enable the practice of giving way to passion.

  1. Notice where we are holding back and let go. Release those barriers of self that keep us separated and in control. Let the vital energy of passion surge through us and connect us to all beyond.
  2. Participate wholly in the universe of possibility. We should allow ourselves to be a channel to shape the stream of passion into a new expression for the world.

When we are looking for an electric socket for possibility, one way is to let go of the edges of ourselves. Then, when we actively participate by finding our tempo and leaning our bodies to the music, we might be able to gain access to the energy of transformation.

簡單的測量

(從我一個尊敬的作家,賽斯·高汀

工業系統是很懶惰的。它側重於容易衡量的事情:您的打字速度有多快,您在測試中的得分是多少,您有多少關注者?

前進的一種方法之一是學習辨別力。 您可以通過難以衡量的事物來發現被他人忽視的價值。

找到能量和承諾去做其他人在短期內可能不容易衡量的事情,這是產生影響的最佳方式。