Binary-Class Image Classification Model for Car vs. Bike Classification 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 Car vs. Bike Classification Dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The dataset contains 2,000 images of cars and bikes. The research team collected these images to investigate the machine learning model’s ability to understand and distinguish the basic structure of cars and bikes. The research team also made sure that all types of bikes and cars were included for a high degree of variety of cars and bikes.

ANALYSIS: The InceptionV3 model’s performance achieved an accuracy score of 98.56% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 99.17%.

CONCLUSION: In this iteration, the TensorFlow InceptionV3 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Binary classification with numerical features

Dataset Used: Car vs. Bike Classification Dataset

Dataset Reference: https://www.kaggle.com/datasets/utkarshsaxenadn/car-vs-bike-classification-dataset

One source of potential performance benchmarks: https://www.kaggle.com/datasets/utkarshsaxenadn/car-vs-bike-classification-dataset/code

The HTML formatted report can be found here on GitHub.

Binary-Class Image Classification Model for Car vs. Bike Classification 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 Car vs. Bike Classification Dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The dataset contains 2,000 images of cars and bikes. The research team collected these images to investigate the machine learning model’s ability to understand and distinguish the basic structure of cars and bikes. The research team also made sure that all types of bikes and cars were included for a high degree of variety of cars and bikes.

ANALYSIS: The NASNetMobile model’s performance achieved an accuracy score of 98.53% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 95.33%.

CONCLUSION: In this iteration, the TensorFlow NASNetMobile CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Binary classification with numerical features

Dataset Used: Car vs. Bike Classification Dataset

Dataset Reference: https://www.kaggle.com/datasets/utkarshsaxenadn/car-vs-bike-classification-dataset

One source of potential performance benchmarks: https://www.kaggle.com/datasets/utkarshsaxenadn/car-vs-bike-classification-dataset/code

The HTML formatted report can be found here on GitHub.

Binary-Class Image Classification Model for Car vs. Bike Classification 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 Car vs. Bike Classification Dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The dataset contains 2,000 images of cars and bikes. The research team collected these images to investigate the machine learning model’s ability to understand and distinguish the basic structure of cars and bikes. The research team also made sure that all types of bikes and cars were included for a high degree of variety of cars and bikes.

ANALYSIS: The ResNet50V2 model’s performance achieved an accuracy score of 98.35% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 95.33%.

CONCLUSION: In this iteration, the TensorFlow ResNet50V2 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Binary classification with numerical features

Dataset Used: Car vs. Bike Classification Dataset

Dataset Reference: https://www.kaggle.com/datasets/utkarshsaxenadn/car-vs-bike-classification-dataset

One source of potential performance benchmarks: https://www.kaggle.com/datasets/utkarshsaxenadn/car-vs-bike-classification-dataset/code

The HTML formatted report can be found here on GitHub.

Binary-Class Image Classification Model for Car vs. Bike Classification 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 Car vs. Bike Classification Dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The dataset contains 2,000 images of cars and bikes. The research team collected these images to investigate the machine learning model’s ability to understand and distinguish the basic structure of cars and bikes. The research team also made sure that all types of bikes and cars were included for a high degree of variety of cars and bikes.

ANALYSIS: The EfficientNetV2M model’s performance achieved an accuracy score of 98.47% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 95.00%.

CONCLUSION: In this iteration, the TensorFlow EfficientNetV2M CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Binary classification with numerical features

Dataset Used: Car vs. Bike Classification Dataset

Dataset Reference: https://www.kaggle.com/datasets/utkarshsaxenadn/car-vs-bike-classification-dataset

One source of potential performance benchmarks: https://www.kaggle.com/datasets/utkarshsaxenadn/car-vs-bike-classification-dataset/code

The HTML formatted report can be found here on GitHub.

Binary-Class Image Classification Model for Car vs. Bike Classification 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 Car vs. Bike Classification Dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The dataset contains 2,000 images of cars and bikes. The research team collected these images to investigate the machine learning model’s ability to understand and distinguish the basic structure of cars and bikes. The research team also made sure that all types of bikes and cars were included for a high degree of variety of cars and bikes.

ANALYSIS: The Xception model’s performance achieved an accuracy score of 99.56% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 98.33%.

CONCLUSION: In this iteration, the TensorFlow Xception CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Binary classification with numerical features

Dataset Used: Car vs. Bike Classification Dataset

Dataset Reference: https://www.kaggle.com/datasets/utkarshsaxenadn/car-vs-bike-classification-dataset

One source of potential performance benchmarks: https://www.kaggle.com/datasets/utkarshsaxenadn/car-vs-bike-classification-dataset/code

The HTML formatted report can be found here on GitHub.

Michael Bungay Stanier on Do More Great Work, Part 5

In his book, Do More Great Work.: Stop the Busywork, and Start the Work that Matters, Michael Bungay Stanier shares his inspiration and techniques to help us do more work that matters.

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

Map 4 Who’s Great

Tapping the power of role models can encourage and pull us forward to the same standard of whatever they embody.

Michael suggests we consider the following steps when identifying heroes and their best work.

  1. Think of eight heroes, role models we think are inspiring for one reason or other.
  2. Role models do not have to be people as long as they resonate with us.
  3. Narrow that list to five.
  4. For each hero, list four of the characteristics that are so inspiring to us.

As we work through the list, we want to gain insights from the following:

  • Looking for patterns in our lineup of role models. Recurring themes or words can give clues about what we believe is essential and where the seeds of our Great Work might lie.
  • When we are feeling discombobulated and unsure of our next move, bring one of our role models to mind and ask:
    • How would [hero] behave right now?
    • What would [hero] do?

To take the hero concept even further, we should:

  1. Know that we are role models to others.
  2. Turn up the volume! Choose a characteristic that one of our heroes embodies.
  3. Build a picture montage of role models.

We want to answer the following questions by expanding and cementing the insights offered by the exercise.

  • What was most powerful about listing our heroes?
  • Who surprised us by showing up on our list? What surprised us?
  • What characteristic showed up that we have and that we take for granted?
  • What is become more precise about who we are and whom we want to be?

“我沒那麼聰明”

(從我一個尊敬的作家,賽斯·高汀

前幾天有人這麼對我說,這實在令人心碎。

在我們的文化中,需要一個天生就具有非凡才能的人的任務數量確實很少。

幾乎我們需要人們做的所有其他事情都是經過努力、實踐和關懷的結果。確實,這項工作的變化也許對某些人來說更容易,但沒有人覺得這每一切都很容易。

其他正確的說法也許是,“我不在乎那麼多。” 我不在乎讀書、一路失敗、露面、做出承諾、邊走邊學、面對失敗、在工作中做得更好。

所有這些都可能都是真實的。

但你幾乎可以肯定我們都有足夠聰明。

Multi-Class Image Classification Model for American Sign Language Alphabet 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 American Sign Language Alphabet Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The dataset contains over 22,000 images of alphabets from American Sign Language, separated into 29 folders that represent the various classes. The research team collected these images to investigate the possibilities of reducing the communication gap between sign-language users and non-Sign language users.

ANALYSIS: The InceptionV3 model’s performance achieved an accuracy score of 99.13% after three epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 89.11%.

CONCLUSION: In this iteration, the TensorFlow InceptionV3 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: American Sign Language Alphabet Dataset

Dataset Reference: https://www.kaggle.com/datasets/debashishsau/aslamerican-sign-language-aplhabet-dataset

One source of potential performance benchmarks: https://www.kaggle.com/datasets/debashishsau/aslamerican-sign-language-aplhabet-dataset/code

The HTML formatted report can be found here on GitHub.