下一個大的創意

(從我一個尊敬的作家,賽斯·高汀)

我們通常有兩個困惑。首先是下一個大創意必須是完完全全原創的。第二是沒有人來競爭。

這幾乎從來沒有這種情況。

汽車不是並亨利福特發明的,在 Facebook佔據主導地位之前,社交網絡已經有很多。 Madam C. J. Walker沒有發明護髮用品,雷·克洛克也絕對沒有發明漢堡包或炸薯條。

對於那些有規模管理並蓬勃發展的本地企業而言,情況更是如此。

所有這些類型的組織的未來都不是基於客戶缺乏選擇。它基於能牽引客戶。

當有一個令人信服的理由時,通常是由於執行、關懷,與人員素質(結合網絡效應),那麼一個新組織就會蓬勃發展。因為人們想要它所提供的東西。

一旦你意識到你不是在尋找一個完全原創和沒見過的東西,你就有無數的選擇。因為機會就是如何簡單地去解決問題,以領導力和慷慨出現在世界上,並做出人們選擇的事情。

困難的部分是展現出領導的能力。

我們一直被灌輸加入“安全”的冒險,而不是尋找值得領導的東西。

這就是創新經常停滯的原因。因為去懷疑會比說“我在領導”來的更容易。

項目在早期階段經常失敗的原因是,領導者可能會害怕有競爭和做選擇,而實際上競爭和選擇才是你在做某事的徵兆。

在我們生活中的所有創傷和變化中,我們都處於新商業模式、新融資模式和社區新生活方式的大量繁殖的風口浪尖上。如果你一直在等待開始一個比你打工來的貢獻更大的項目,這個時候真的是我能回憶起來中的最好的時機。

Binary Class Image Classification Deep Learning Model for Meat Quality Assessment Using TensorFlow Take 5

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

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Meat Quality Assessment dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The research team collected the dataset to develop a meat quality assessment system based on deep learning. The published paper explains all of the experimental results, proving the usability of the dataset and model. This dataset contains fresh and spoiled red meat samples from a supermarket in Izmir, Turkey, for a university-industry collaboration project at the Izmir University of Economics.

For this modeling project, we will predict whether an image represents a fresh or spoiled meat case. In this iteration, we will construct a CNN model based on the DenseNet201 architecture to make predictions.

ANALYSIS: In this iteration, the DenseNet201 model’s performance achieved an accuracy score of 99.80% after 10 epochs using the training dataset. The same model processed the validation dataset with an accuracy rate of 94.97%.

CONCLUSION: In this iteration, the DenseNet201-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: Meat Quality Assessment

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference: https://www.kaggle.com/crowww/meat-quality-assessment-based-on-deep-learning

One potential source of performance benchmarks: https://ieeexplore.ieee.org/abstract/document/8946388

The HTML formatted report can be found here on GitHub.

Binary Class Image Classification Deep Learning Model for Meat Quality Assessment Using TensorFlow Take 4

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

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Meat Quality Assessment dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The research team collected the dataset to develop a meat quality assessment system based on deep learning. The published paper explains all of the experimental results, proving the usability of the dataset and model. This dataset contains fresh and spoiled red meat samples from a supermarket in Izmir, Turkey, for a university-industry collaboration project at the Izmir University of Economics.

For this modeling project, we will predict whether an image represents a fresh or spoiled meat case. In this iteration, we will construct a CNN model based on the EfficientNetB5 architecture to make predictions.

ANALYSIS: In this iteration, the EfficientNetB5 model’s performance achieved an accuracy score of 94.86% after 10 epochs using the training dataset. The same model processed the validation dataset with an accuracy rate of 98.68%.

CONCLUSION: In this iteration, the EfficientNetB5-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: Meat Quality Assessment

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference: https://www.kaggle.com/crowww/meat-quality-assessment-based-on-deep-learning

One potential source of performance benchmarks: https://ieeexplore.ieee.org/abstract/document/8946388

The HTML formatted report can be found here on GitHub.

Binary Class Image Classification Deep Learning Model for Meat Quality Assessment Using TensorFlow Take 3

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

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Meat Quality Assessment dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The research team collected the dataset to develop a meat quality assessment system based on deep learning. The published paper explains all of the experimental results, proving the usability of the dataset and model. This dataset contains fresh and spoiled red meat samples from a supermarket in Izmir, Turkey, for a university-industry collaboration project at the Izmir University of Economics.

For this modeling project, we will predict whether an image represents a fresh or spoiled meat case. In this iteration, we will construct a CNN model based on the VGG19 architecture to make predictions.

ANALYSIS: In this iteration, the VGG19 model’s performance achieved an accuracy score of 92.36% after 15 epochs using the training dataset. The same model processed the validation dataset with an accuracy rate of 100.00%.

CONCLUSION: In this iteration, the VGG19-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: Meat Quality Assessment

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference: https://www.kaggle.com/crowww/meat-quality-assessment-based-on-deep-learning

One potential source of performance benchmarks: https://ieeexplore.ieee.org/abstract/document/8946388

The HTML formatted report can be found here on GitHub.

Binary Class Image Classification Deep Learning Model for Meat Quality Assessment Using TensorFlow Take 2

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

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Meat Quality Assessment dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The research team collected the dataset to develop a meat quality assessment system based on deep learning. The published paper explains all of the experimental results, proving the usability of the dataset and model. This dataset contains fresh and spoiled red meat samples from a supermarket in Izmir, Turkey, for a university-industry collaboration project at the Izmir University of Economics.

For this modeling project, we will predict whether an image represents a fresh or spoiled meat case. In this iteration, we will construct a CNN model based on the ResNet152V2 architecture to make predictions.

ANALYSIS: In this iteration, the ResNet152V2 model’s performance achieved an accuracy score of 98.88% after 15 epochs using the training dataset. The same model processed the validation dataset with an accuracy rate of 88.10%.

CONCLUSION: In this iteration, the ResNet152V2-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: Meat Quality Assessment

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference: https://www.kaggle.com/crowww/meat-quality-assessment-based-on-deep-learning

One potential source of performance benchmarks: https://ieeexplore.ieee.org/abstract/document/8946388

The HTML formatted report can be found here on GitHub.

Binary Class Image Classification Deep Learning Model for Meat Quality Assessment Using TensorFlow Take 1

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

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Meat Quality Assessment dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The research team collected the dataset to develop a meat quality assessment system based on deep learning. The published paper explains all of the experimental results, proving the usability of the dataset and model. This dataset contains fresh and spoiled red meat samples from a supermarket in Izmir, Turkey, for a university-industry collaboration project at the Izmir University of Economics.

For this modeling project, we will predict whether an image represents a fresh or spoiled meat case. In this iteration, we will construct a CNN model based on the InceptionV3 architecture to make predictions.

ANALYSIS: In this iteration, the InceptionV3 model’s performance achieved an accuracy score of 99.80% after 15 epochs using the training dataset. The same model processed the validation dataset with an accuracy rate of 92.86%.

CONCLUSION: In this iteration, the InceptionV3-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: Meat Quality Assessment

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference: https://www.kaggle.com/crowww/meat-quality-assessment-based-on-deep-learning

One potential source of performance benchmarks: https://ieeexplore.ieee.org/abstract/document/8946388

The HTML formatted report can be found here on GitHub.

Seth Godin on Survival Is Not Enough, Part 1

In his book, Survival Is Not Enough: Why Smart Companies Abandon Worry and Embrace Change, Seth Godin discusses how innovative organizations and individuals can apply prudent strategies in adapting and positioning themselves for the constant changes.

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

Chapter 1, Change

In this chapter, Seth discusses why change is no longer something we can control with absolute certainty and the better strategies to deal with changes. He offers the following observations and recommendations for us to think about:

  • Many companies are not organized for change because they have never needed to be. Growing and profiting from stable times was a terrific strategy.
  • Unfortunately, change is now constant. The fundamental ideas we have used to build our companies and careers are quickly going out of style. The world is changing on our watch, and it is not fun.
  • Seth outlines the four structural changes in the business that have taken place in the last several decades:
    • “The speed at which we make decisions is now the factor that limits the speed of business.” Our decisions are on the critical path of getting things done, and the lead time for many things we do has shrunk dramatically.
    • “The Net has made information close to free and close to ubiquitous, further fueling the need for speed.”
    • Our worldview of having islands of stability is disappearing. “There’s only one market, and it’s the whole world.”
    • “Metcalfe’s law (networks get more powerful when they connect more people) has reached infinity.” As a result, we are all virtually connected.
  • For a long time, owning physical plants and factories has been the best way to get rich. However, with rich connectivity and cheap shipping, the factory-centric model is dying. Being factory-centric does not increase our profits but decreases them. The Factory-centric model also does not lessen our time to market; it now likely increases it.
  • Surviving change is a noble goal, but we need to find ways to embrace changes and get better results. We need to turn working with changes into a positive feedback loop.
  • When people start interacting in a positive feedback loop, the loop can get amplified and enter a stage called runaway. Even though runaway cannot last forever, it is fun while it lasts. So our job should be to figure out how to trigger a runaway, do it again later, and create a never-ending series of positive feedback loops and runaway successes.
  • Our fear of changing a used-to-be successful winning strategy combined with our reliance on command-and-control tactics makes it hard for us to embrace change. Evolution (inheritable modifications over many generations) is the most potent tactic for dealing with change. We should apply this proven, organic technique to embrace change, not fight it.

In summary, Seth suggests that we take an active role in embracing change. “Change is out of our control, and the way we deal with change is outmoded and ineffective. Our organizations assume that we live with a different, slower time cycle.”

用努力去追求質量

(從我一個尊敬的作家,賽斯·高汀

質量的定義是始終符合規範。並做出與遵守可衡量的承諾。

努力是當我們超出正常速度時會發生的事情。當我們進行體力或情感勞動來深入挖掘,並且專注在與眾不同的事情時。努力與滑行是相反的。

我們經常被教導質量是努力的結果。如果你只是更加努力地嘗試,你就會更接近需要滿足的規範。

然而,當我們查看以質量著稱的組織、品牌或個人時,我們完全不清楚他們是否付出了更多的努力。因為這種努力是根本不可能永久持續的下。

在凌志工廠工作的人在一天結束時並不比製造凱迪拉克的工人來的更累。這與努力無關。對於從不錯過交貨的 Dabbawalla 來說也是如此。事實上,如果只專注於努力(以及您團隊的努力),這幾乎可以保證您的質量問題將持續存在。

持續存在的質量問題是一個系統性問題,如果您不在系統上下功夫,您就不會改進不了它。

“我們該怎麼做這項工作?”是比一個“我們該怎麼更努力去做?”更好的問題。