Binary Class Image Classification Deep Learning Model for CycleGAN Van Gogh vs. Photo 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 CycleGAN Cezanne vs. Photo dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains images from two classes A and B (for example, apple vs. orange, horses vs. zebras, and so on). The researchers used the images to train machine learning models for research work in General Adversarial Networks (GAN).

In this iteration, we will construct a CNN model based on the DenseNet121 architecture to make predictions.

ANALYSIS: In this iteration, the DenseNet121 model’s performance achieved an accuracy score of 99.40% after ten epochs using the training dataset. The same model processed the validation dataset with an accuracy measurement of 97.16%. Finally, the final model processed the test dataset with an accuracy score of 92.44%.

CONCLUSION: In this iteration, the DenseNet121-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: CycleGAN Van Gogh vs. Photo Dataset

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference: https://people.eecs.berkeley.edu/%7Etaesung_park/CycleGAN/datasets/

One potential source of performance benchmarks: https://arxiv.org/abs/1703.10593 or https://junyanz.github.io/CycleGAN/

The HTML formatted report can be found here on GitHub.

Algorithmic Trading Model using Force Index with Stop Loss

NOTE: This script is for learning purposes only and does not constitute a recommendation for buying or selling any stock mentioned in this script.

SUMMARY: This project aims to construct and test an algorithmic trading model and document the end-to-end steps using a template.

INTRODUCTION: This algorithmic trading model uses force index indicators to employ a simple mean-reversion strategy for stock position entries and exits. For the Force Index indicator, the model will use a 13-period indicator for the trading signal. The model will initiate a long position when the trading indicator turns from negative to positive. Conversely, the model will exit the long position when the signal indicator turns from positive to negative.

Moreover, one of the two models will exit the long position when a 10-percent stop-loss triggers. We will evaluate the effectiveness of using a stop-loss trigger by comparing the portfolio results to the model without the stop-loss trigger.

ANALYSIS: In this modeling iteration, we analyzed ten stocks for ten years between September 1, 2011, and September 24, 2021. The models’ performance appeared at the end of the script. The models with the wider signal line width generally produced a better return for the tested stocks. Moreover, the simple buy-and-hold approach came out ahead for all stocks.

CONCLUSION: For most stocks during the modeling time frame, the long-only trading strategy with the Force Index did not produce a better return than the buy-and-hold approach. We should consider modeling these stocks further by experimenting with more variations of the strategy.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Quandl

The HTML formatted report can be found here on GitHub.

Binary Class Image Classification Deep Learning Model for CycleGAN Van Gogh vs. Photo 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 CycleGAN Cezanne vs. Photo dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains images from two classes A and B (for example, apple vs. orange, horses vs. zebras, and so on). The researchers used the images to train machine learning models for research work in General Adversarial Networks (GAN).

In this iteration, we will construct a CNN model based on the ResNet50V2 architecture to make predictions.

ANALYSIS: In this iteration, the ResNet50V2 model’s performance achieved an accuracy score of 99.35% after ten epochs using the training dataset. The same model processed the validation dataset with an accuracy measurement of 98.18%. Finally, the final model processed the test dataset with an accuracy score of 98.69%.

CONCLUSION: In this iteration, the ResNet50V2-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: CycleGAN Van Gogh vs. Photo Dataset

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference: https://people.eecs.berkeley.edu/%7Etaesung_park/CycleGAN/datasets/

One potential source of performance benchmarks: https://arxiv.org/abs/1703.10593 or https://junyanz.github.io/CycleGAN/

The HTML formatted report can be found here on GitHub.

Binary Class Image Classification Deep Learning Model for CycleGAN Van Gogh vs. Photo 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 CycleGAN Cezanne vs. Photo dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains images from two classes A and B (for example, apple vs. orange, horses vs. zebras, and so on). The researchers used the images to train machine learning models for research work in General Adversarial Networks (GAN).

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 99.50% after ten epochs using the training dataset. The same model processed the validation dataset with an accuracy measurement of 99.25%. Finally, the final model processed the test dataset with an accuracy score of 92.96%.

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: CycleGAN Van Gogh vs. Photo Dataset

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference: https://people.eecs.berkeley.edu/%7Etaesung_park/CycleGAN/datasets/

One potential source of performance benchmarks: https://arxiv.org/abs/1703.10593 or https://junyanz.github.io/CycleGAN/

The HTML formatted report can be found here on GitHub.

Jeff Goins on Real Artists Don’t Starve, Part 7

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 7, Collaborate with Others

In this chapter, Jeff discusses the importance of collaborating with others for thriving artists. He offers the following recommendations for us to think about:

  • While some successful geniuses chose to work alone, many did their best work when collaborating with others. The new definition of an artist is a visionary who brings people and resources together to create opportunities for our work to flourish. Thus, our success is closely related to our ability to work well with others.
  • Creative output is often a slow and grueling endeavor and, at times, can feel discouraging. However, during those painful moments, we need people to correct our path. Furthermore, creativity works best when it originates from a small community circle instead of a solitary invention.
  • Sometimes we need more than just a loose collective of peers to help us succeed. For those occasions, we might need a more formal group of coworkers or business partners to help us realize our vision. Hire professional help, coordinate, and integrate their work with ours is the job of an artist.
  • If we want to do world-changing creative work, we might need to accept the reality that we likely will not be able to do it alone. So it is the thriving artist’s job to cultivate the circles of collaboration and create a sense of accountability that could drive everyone in the circle to create better work.

In summary, “The Starving Artist always works alone. The Thriving Artist collaborates with others.”

減速帶

(從我一個尊敬的作家,賽斯·高汀

我們要么走,要么不走。 我們每天都要做出這個決定。 也許你已經決定:

我們要走。

我們所走過的彎路,我們所勉強避開的坑洼,和出乎意料的中斷。我們經歷了,我們也還決定繼續前行。

減速帶是有存在的。 它們只是警告,或者它們是不可避免的,它們會造成磨損。減速帶也是不能抗拒的。

但是一項來讓我們減速並阻止前進的是障礙物。

通常,減速帶和障礙物之間的唯一區別,只是我們決定該是哪一個。

向前走。

Binary Class Image Classification Deep Learning Model for CycleGAN Cezanne vs. Photo 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 CycleGAN Cezanne vs. Photo dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains images from two classes A and B (for example, apple vs. orange, horses vs. zebras, and so on). The researchers used the images to train machine learning models for research work in General Adversarial Networks (GAN).

In this iteration, we will construct a CNN model based on the VGG16 architecture to make predictions.

ANALYSIS: In this iteration, the VGG16 model’s performance achieved an accuracy score of 98.46% after ten epochs using the training dataset. The same model processed the validation dataset with an accuracy measurement of 97.36%. Finally, the final model processed the test dataset with an accuracy score of 99.01%.

CONCLUSION: In this iteration, the VGG16-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: CycleGAN Cezanne vs. Photo Dataset

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference: https://people.eecs.berkeley.edu/%7Etaesung_park/CycleGAN/datasets/

One potential source of performance benchmarks: https://arxiv.org/abs/1703.10593 or https://junyanz.github.io/CycleGAN/

The HTML formatted report can be found here on GitHub.

Binary Class Image Classification Deep Learning Model for CycleGAN Cezanne vs. Photo 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 CycleGAN Cezanne vs. Photo dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains images from two classes A and B (for example, apple vs. orange, horses vs. zebras, and so on). The researchers used the images to train machine learning models for research work in General Adversarial Networks (GAN).

In this iteration, we will construct a CNN model based on the DenseNet121 architecture to make predictions.

ANALYSIS: In this iteration, the DenseNet121 model’s performance achieved an accuracy score of 99.80% after ten epochs using the training dataset. The same model processed the validation dataset with an accuracy measurement of 98.90%. Finally, the final model processed the test dataset with an accuracy score of 99.87%.

CONCLUSION: In this iteration, the DenseNet121-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: CycleGAN Cezanne vs. Photo Dataset

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference: https://people.eecs.berkeley.edu/%7Etaesung_park/CycleGAN/datasets/

One potential source of performance benchmarks: https://arxiv.org/abs/1703.10593 or https://junyanz.github.io/CycleGAN/

The HTML formatted report can be found here on GitHub.