Jeff Goins on Real Artists Don’t Starve, Part 12

In his book, Real Artists Don’t Starve: Timeless Strategies for Thriving in the New Creative Age, Jeff Goins discusses how we can apply prudent strategies in positioning ourselves for thriving in our chosen field of craft.

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

Chapter 12, Make Money to Make Art

In this chapter, Jeff discusses the relationship between making art and making money. He offers the following key points for us to think about:

  • For many years, the primary economic model for art-making was to facilitate gift exchange. Only recently did we start thinking art was something we could charge money for.
  • Thriving artists know that making money was never the point of making art. The point is to share the gift of art. At the same time, the artists also need to be financially viable. The challenge becomes that money is the mean of making art, but money must never be the master.
  • Creative work is a costly endeavor, both in time and financial resources. The work also requires us to dedicate large amounts of our lives to it without any immediate reward. So when we find breakthroughs to make money, it buys us time and allows us to create more art.
  • For the art form of kamishibai, the candy sales made the art possible. Furthermore, the business of kamishibai showing made the creative side possible. The later generations of artists went on to spread a new form of art called “manga.” The older art form launched an entirely new genre that continues today.
  • Thriving artists know we must make money to make art, but we should not make income generation the only or utmost objective. Money exists, in an artist’s world, to buy her another opportunity to make art. Every chance we can create instead of just scrambling for a living is a win. With time, those wins add up for the side of art-making.

In summary, “The Starving Artist despises the need for money. The Thriving Artist makes money to make art.”

做過閱讀

(從我一個尊敬的作家,賽斯·高汀

這是必不可少的。

領域知識是一份禮物。這就是我們能在某個領域和社會之中取得進步的方式。前人的見解和錯誤的步驟都清清楚楚地列出,有待我們來學習。

但這也可成為一個陷阱。

因為你曾經能夠先做所有的閱讀。在寫自己想寫的科幻書籍之前,您可以先閱讀所有重要的科幻書籍。您可以在導演之前先觀看所有關鍵電影。在您開藥給病人之前,您可以先了解醫學領域的所有當前想法…

但那種時光已不在。

有些人對可用的“閱讀”長尾做出這回應,那就決定什麼都不做,就好像天真的相信一個初學者的想法是合適給專業人士來利用的策略。

還有些人的回應是簡簡單單地僵在原地,要求一定要先擁有完美的知識後,才能在做出決斷。

顯然的是,成功的道路是建在這些極端曲線之的某個地方。

在閱讀的過程中,有些新的作品可能會和一些舊的創意會起同韻,就像當您開始看到這些連接點的時候,或是當您突然了解誰以前影響了您現在正在交往的人。

那個時刻就是該開始發表您的工作並做出決斷的機會。

Multi-Class Image Classification Deep Learning Model for ISIC Challenge 2018 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 ISIC Challenge 2018 dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The International Skin Imaging Collaboration (ISIC) is an international effort to improve melanoma diagnosis, sponsored by the International Society for Digital Imaging of the Skin (ISDIS). The ISIC Archive contains the most extensive publicly available collection of quality-controlled dermoscopic images of skin lesions. Since 2016, ISIC has sponsored annual challenges for the computer science community associated with leading computer vision conferences.

For this modeling project, we will predict whether an image represents one of seven disease conditions (Task 3: Lesion Diagnosis). 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 a categorical accuracy score of 90.22% after ten epochs using the training dataset. The same model processed the validation dataset with a categorical accuracy rate of 80.83%.

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: ISIC 2018 Challenge Dataset

Dataset ML Model: Multi-Class classification with numerical attributes

Dataset Reference: https://challenge.isic-archive.com/data

One potential source of performance benchmarks: https://challenge.isic-archive.com/leaderboards/2018

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Deep Learning Model for ISIC Challenge 2018 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 ISIC Challenge 2018 dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The International Skin Imaging Collaboration (ISIC) is an international effort to improve melanoma diagnosis, sponsored by the International Society for Digital Imaging of the Skin (ISDIS). The ISIC Archive contains the most extensive publicly available collection of quality-controlled dermoscopic images of skin lesions. Since 2016, ISIC has sponsored annual challenges for the computer science community associated with leading computer vision conferences.

For this modeling project, we will predict whether an image represents one of seven disease conditions (Task 3: Lesion Diagnosis). In this iteration, we will construct a CNN model based on the EfficientNetB7 architecture to make predictions.

ANALYSIS: In this iteration, the EfficientNetB7 model’s performance achieved a categorical accuracy score of 87.10% after ten epochs using the training dataset. The same model processed the validation dataset with a categorical accuracy rate of 84.46%.

CONCLUSION: In this iteration, the EfficientNetB7-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: ISIC 2018 Challenge Dataset

Dataset ML Model: Multi-Class classification with numerical attributes

Dataset Reference: https://challenge.isic-archive.com/data

One potential source of performance benchmarks: https://challenge.isic-archive.com/leaderboards/2018

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Deep Learning Model for ISIC Challenge 2018 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 ISIC Challenge 2018 dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The International Skin Imaging Collaboration (ISIC) is an international effort to improve melanoma diagnosis, sponsored by the International Society for Digital Imaging of the Skin (ISDIS). The ISIC Archive contains the most extensive publicly available collection of quality-controlled dermoscopic images of skin lesions. Since 2016, ISIC has sponsored annual challenges for the computer science community associated with leading computer vision conferences.

For this modeling project, we will predict whether an image represents one of seven disease conditions (Task 3: Lesion Diagnosis). 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 a categorical accuracy score of 81.37% after ten epochs using the training dataset. The same model processed the validation dataset with a categorical accuracy rate of 84.97%.

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: ISIC 2018 Challenge Dataset

Dataset ML Model: Multi-Class classification with numerical attributes

Dataset Reference: https://challenge.isic-archive.com/data

One potential source of performance benchmarks: https://challenge.isic-archive.com/leaderboards/2018

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Deep Learning Model for ISIC Challenge 2018 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 ISIC Challenge 2018 dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The International Skin Imaging Collaboration (ISIC) is an international effort to improve melanoma diagnosis, sponsored by the International Society for Digital Imaging of the Skin (ISDIS). The ISIC Archive contains the most extensive publicly available collection of quality-controlled dermoscopic images of skin lesions. Since 2016, ISIC has sponsored annual challenges for the computer science community associated with leading computer vision conferences.

For this modeling project, we will predict whether an image represents one of seven disease conditions (Task 3: Lesion Diagnosis). 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 a categorical accuracy score of 84.63% after ten epochs using the training dataset. The same model processed the validation dataset with a categorical accuracy rate of 83.42%.

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: ISIC 2018 Challenge Dataset

Dataset ML Model: Multi-Class classification with numerical attributes

Dataset Reference: https://challenge.isic-archive.com/data

One potential source of performance benchmarks: https://challenge.isic-archive.com/leaderboards/2018

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Deep Learning Model for ISIC Challenge 2018 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 ISIC Challenge 2018 dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The International Skin Imaging Collaboration (ISIC) is an international effort to improve melanoma diagnosis, sponsored by the International Society for Digital Imaging of the Skin (ISDIS). The ISIC Archive contains the most extensive publicly available collection of quality-controlled dermoscopic images of skin lesions. Since 2016, ISIC has sponsored annual challenges for the computer science community associated with leading computer vision conferences.

For this modeling project, we will predict whether an image represents one of seven disease conditions (Task 3: Lesion Diagnosis). 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 a categorical accuracy score of 88.32% after ten epochs using the training dataset. The same model processed the validation dataset with a categorical accuracy rate of 82.38%.

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: ISIC 2018 Challenge Dataset

Dataset ML Model: Multi-Class classification with numerical attributes

Dataset Reference: https://challenge.isic-archive.com/data

One potential source of performance benchmarks: https://challenge.isic-archive.com/leaderboards/2018

The HTML formatted report can be found here on GitHub.

Jeff Goins on Real Artists Don’t Starve, Part 11

In his book, Real Artists Don’t Starve: Timeless Strategies for Thriving in the New Creative Age, Jeff Goins discusses how we can apply prudent strategies in positioning ourselves for thriving in our chosen field of craft.

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

Chapter 11, Diversify Your Portfolio

In this chapter, Jeff discusses how thriving artists should handle their projects and portfolios. He offers the following recommendations for us to think about:

  • The rule of portfolio says that we should strive to build a diverse body of work. While the starving artist believes he must master a single skill, the thriving artist masters more than one. Best artists regularly change and evolve; they do not restrict their art to a single form.
  • Thriving artists work like good investors. They do not just live off their art. They keep diverse portfolios and rely on multiple income streams. Building a diverse portfolio requires developing a leaky mental filter for spotting the right places to invest our time and resources.
  • A leaky mental filter is the ability to hold multiple conflicting ideas in tension to create synergy with each other. A skillful exercise of the leaky filter can give us insight into possibility as it allows us to identify new opportunities and take advantage of them.
  • If we want to create enduring work and not just a series of one-hit wonders, we must be open to learning new things. So while starving artists try to master only one skill, thriving artists acquire whatever skills necessary to get the job done.
  • There comes a time not to let our mind wander; instead, we dig in and focus. We focus on developing a body of work rather than just a single creation. Harnessing a distractable mind can be a strength in creative work. We can use our creative quirks to our advantage by identifying opportunities to do fulfilling work that we might have otherwise missed.
  • We must practice using our leaky filters to find new skills, learn them, and apply them. Then, while focusing on the big picture, we will use any skills and tools that will help us develop a more substantial portfolio, which can lead to a lifetime of creation.

In summary, “The Starving Artist masters one craft. The Thriving Artist masters many.”