Thank you for the messages folks!\nAs politely mentioned, I broke the entire codebase up until preprocessing steps in three parts :
\n\nMelanoma - A Story in Three Parts- Part One : DATA SET CONSTRUCTION\n( https://www.kaggle.com/fireheart7/melanoma-a-story-in-3-parts-part-one )
Melanoma - A Story in Three Parts- Part Two : EXPLORATORY DATA ANALYSIS\n( https://www.kaggle.com/fireheart7/melanoma-a-story-in-3-parts-part-two?scriptVersionId=38737733 )
Melanoma - A Story in Three Parts- Part Three : ** IMAGE PRE-PROCESSING AND MODEL CONSTRUCTION**\n( https://www.kaggle.com/fireheart7/melanoma-a-story-in-3-parts-part-three?scriptVersionId=38742041 )
Along with it, the data set that encapsulates information about the images have been uploaded here
\n\nhttps://www.kaggle.com/fireheart7/melanoma-image-insights
\n\nThe third module is yet to finish! However, the first two are nearly complete!!
\n\nThanks to the community so far. I learned a lot and shout out to these amazing authors have been given in these notebooks itself.
\n\nI have tried to explain everything in the notebooks to the best of my understanding, especially in book two and three!. Even if you are new, the concepts shouldn't bounce too much over!!
\n\nPlease upvote if you find it useful. Keeps me motivated.
\n\nHappy Learning!
", "messages": [ { "id": "929211", "postDate": "07/14/2020 14:27:50", "content": "Thank you for the messages folks!\nAs politely mentioned, I broke the entire codebase up until preprocessing steps in three parts :
\n\nMelanoma - A Story in Three Parts- Part One : DATA SET CONSTRUCTION\n( https://www.kaggle.com/fireheart7/melanoma-a-story-in-3-parts-part-one )
Melanoma - A Story in Three Parts- Part Two : EXPLORATORY DATA ANALYSIS\n( https://www.kaggle.com/fireheart7/melanoma-a-story-in-3-parts-part-two?scriptVersionId=38737733 )
Melanoma - A Story in Three Parts- Part Three : ** IMAGE PRE-PROCESSING AND MODEL CONSTRUCTION**\n( https://www.kaggle.com/fireheart7/melanoma-a-story-in-3-parts-part-three?scriptVersionId=38742041 )
Along with it, the data set that encapsulates information about the images have been uploaded here
\n\nhttps://www.kaggle.com/fireheart7/melanoma-image-insights
\n\nThe third module is yet to finish! However, the first two are nearly complete!!
\n\nThanks to the community so far. I learned a lot and shout out to these amazing authors have been given in these notebooks itself.
\n\nI have tried to explain everything in the notebooks to the best of my understanding, especially in book two and three!. Even if you are new, the concepts shouldn't bounce too much over!!
\n\nPlease upvote if you find it useful. Keeps me motivated.
\n\nHappy Learning!
", "rawMarkdown": "Thank you for the messages folks!\nAs politely mentioned, I broke the entire codebase up until preprocessing steps in three parts : \n\n1. Melanoma - A Story in Three Parts- Part One : **DATA SET CONSTRUCTION**\n( **https://www.kaggle.com/fireheart7/melanoma-a-story-in-3-parts-part-one** )\n\n2. Melanoma - A Story in Three Parts- Part Two : **EXPLORATORY DATA ANALYSIS**\n( **https://www.kaggle.com/fireheart7/melanoma-a-story-in-3-parts-part-two?scriptVersionId=38737733** )\n\n3. Melanoma - A Story in Three Parts- Part Three : ** IMAGE PRE-PROCESSING AND MODEL CONSTRUCTION**\n( **https://www.kaggle.com/fireheart7/melanoma-a-story-in-3-parts-part-three?scriptVersionId=38742041** )\n\nAlong with it, the data set that encapsulates information about the images have been uploaded here \n\n**https://www.kaggle.com/fireheart7/melanoma-image-insights**\n\n\nThe third module is yet to finish! However, the first two are nearly complete!!\n\nThanks to the community so far. I learned a lot and shout out to these amazing authors have been given in these notebooks itself.\n\n**I have tried to explain everything in the notebooks to the best of my understanding, especially in book two and three!**. Even if you are new, the concepts shouldn't bounce too much over!! \n\n\n*Please upvote if you find it useful. Keeps me motivated.*\n\nHappy Learning!", "votes": null }, { "id": "929532", "postDate": "07/14/2020 18:22:00", "content": "Great story. I learned a bunch of things reading it. I particularly like your comparisons of \"mean channel intensities\" malignant versus benign in part 2. I like your image size comparisons of train versus test. And I like k-means clustering in part 3. I didn't know you could do that with open cv.
\n\nNote that you have disabled comments on your notebooks so I wasn't able to leave any comments. Explanation here
", "rawMarkdown": "Great story. I learned a bunch of things reading it. I particularly like your comparisons of \"mean channel intensities\" malignant versus benign in part 2. I like your image size comparisons of train versus test. And I like k-means clustering in part 3. I didn't know you could do that with open cv.\n\nNote that you have disabled comments on your notebooks so I wasn't able to leave any comments. Explanation [here][1]\n\n[1]: https://www.kaggle.com/product-feedback/166966", "votes": null }, { "id": "931959", "postDate": "07/16/2020 15:22:13", "content": "Thank you so much!!
", "rawMarkdown": "Thank you so much!!", "votes": null } ], "comments": [ { "id": 929532, "author_name": "cdeotte", "author_url": "", "post_date": "07/14/2020 18:22:00", "content": "Great story. I learned a bunch of things reading it. I particularly like your comparisons of \"mean channel intensities\" malignant versus benign in part 2. I like your image size comparisons of train versus test. And I like k-means clustering in part 3. I didn't know you could do that with open cv.
\n\nNote that you have disabled comments on your notebooks so I wasn't able to leave any comments. Explanation here
", "votes": null, "replies": [ { "id": 931959, "author_name": "fireheart7", "author_url": "", "post_date": "07/16/2020 15:22:13", "content": "Thank you so much!!
", "votes": null, "replies": [] } ] } ], "raw_markdown_by_id": { "929211": "Thank you for the messages folks!\nAs politely mentioned, I broke the entire codebase up until preprocessing steps in three parts : \n\n1. Melanoma - A Story in Three Parts- Part One : **DATA SET CONSTRUCTION**\n( **https://www.kaggle.com/fireheart7/melanoma-a-story-in-3-parts-part-one** )\n\n2. Melanoma - A Story in Three Parts- Part Two : **EXPLORATORY DATA ANALYSIS**\n( **https://www.kaggle.com/fireheart7/melanoma-a-story-in-3-parts-part-two?scriptVersionId=38737733** )\n\n3. Melanoma - A Story in Three Parts- Part Three : ** IMAGE PRE-PROCESSING AND MODEL CONSTRUCTION**\n( **https://www.kaggle.com/fireheart7/melanoma-a-story-in-3-parts-part-three?scriptVersionId=38742041** )\n\nAlong with it, the data set that encapsulates information about the images have been uploaded here \n\n**https://www.kaggle.com/fireheart7/melanoma-image-insights**\n\n\nThe third module is yet to finish! However, the first two are nearly complete!!\n\nThanks to the community so far. I learned a lot and shout out to these amazing authors have been given in these notebooks itself.\n\n**I have tried to explain everything in the notebooks to the best of my understanding, especially in book two and three!**. Even if you are new, the concepts shouldn't bounce too much over!! \n\n\n*Please upvote if you find it useful. Keeps me motivated.*\n\nHappy Learning!", "929532": "Great story. I learned a bunch of things reading it. I particularly like your comparisons of \"mean channel intensities\" malignant versus benign in part 2. I like your image size comparisons of train versus test. And I like k-means clustering in part 3. I didn't know you could do that with open cv.\n\nNote that you have disabled comments on your notebooks so I wasn't able to leave any comments. Explanation [here][1]\n\n[1]: https://www.kaggle.com/product-feedback/166966", "931959": "Thank you so much!!" }, "source": "meta" }