'
\nListen up, guys! These are the jupyter notebooks you'll need to start learning about the tpu-getting-started competition.
\nPay attention, because if you don't know how to use them by the time we're done, you are missing out!
Enjoy! <3
\nRotation Augmentation Gpu Tpu 0 96 by cdeotte
\n\n\ncdeotte used rotation augmentation on the TPU
\n
This notebook will show you how to improve the accuracy of a deep learning model using augmentation techniques. You will learn how to perform image rotations, zooming, and translation using TensorFlow's built-in transformations library. You will also learn how to use a GPU or TPU for training your models with improved speed and accuracy.
\nCutmix And Mixup On Gpu Tpu by cdeotte
\n\n\nThis notebook demonstrates how to use the cut-mix-and-mixup algorithm on a GPU or TPU.
\n
Same as before just with different augmentation methods. To get the best performance results, it is often recommended to use a GPU or TPU. However, if you are limited to a CPU, this notebook will show you how to get good results with a cut-mix-and-mixup approach.
\nTfrecords Basics by ryanholbrook
\n\n\nTfrecords-Basics: This notebook explains how to read and write data in the TFRecord format.
\n
tfrecords is a powerful way to save and load data in TensorFlow. This notebook will walk you through the basics of working with tfrecords files, including how to create them, how to load them, and how to use them with TensorFlow.
\nComputer Vision Petals To The Metal by georgezoto
\n\n\nThis notebook contains code that allows you to submit predictions for a computer vision competition.
\n
If you want to learn how to solve a computer vision problem, this is the notebook for you. georgezoto walks you through every step of the process, from loading the data to making submissions to the competition leaderboard. There's also plenty of room for improvement - so don't stop at just one submission! georgezoto's notebook will help you climb up the leaderboard and learn some essential skills in the process.
\nFc Ensemble External Data Effnet Densenet by atamazian
\n\n\natamazian combined a lot of different models to make a better model that predict better.
\n
If you are looking to train a deep learning model on a large dataset, and want to ensure good accuracy without spending too much time on tuning hyperparameters, then ensemble learning is a great option. In this notebook, we walk through an example of using two deep neural networks models (densenet and effneet), to achieve good predictive accuracy.
\nFlower Classification With Tpus Eda And Baseline by dimitreoliveira
\n\n\ndimitreoliveira created a baseline on the TPU to help identify flowers using.
\n
If you're looking to push the boundaries of machine learning and deep learning, dimitreoliveira's notebook is the perfect place to start. Featuring a state-of-the-art TensorFlow implementation, this notebook will show you how to train a model on a real-world dataset. You'll also learn how to evaluate your model and make predictions on new data.
\nJax Flax Tf Data Vision Transformers Tutorial by odins0n
\n\n\nodins0n shows us how to use Tf-Data, Jax and Flax to do predictions on images.
\n
If you are looking to get started with deep learning and image data, this is a great tutorial to get started. You will learn how to use the Jax-Flax library as well as the TensorFlow data pipeline. You will also learn how to use the Vision Transformers library to improve your predictions.
\nA Simple Petals TF2 Notebook by philculliton
\n\n\nphilculliton used the code provided to train a TF2 model on a set of images.
\n
This notebook guides you through training a simple convolutional neural network for classifying flowers using the TensorFlow 2.1 API. You'll learn how to construct a model, train it on data, and evaluate its performance. The code examples are rich and easy to follow, making this an excellent resource for anyone looking to get started with deep learning and TensorFlow 2.1.
\nTf Hybrid Efficientnet Swin Transformer Gradcam by ipythonx
\n\n\nipythonx used a model called Swin combined with efficientnet to create an ensemble.
\n
This is one great notebook on using TensorFlow, the Hybrid-Efficientnet, and Swin Transformer for gradcam. ipythonx has clearly taken a lot of time and effort in putting this together, and it is definitely worth going through. The code is explained very well, and it's easy to follow along. You'll learn a lot by going through this notebook!
\nFlower With Tpus Advanced Augmentations by dimitreoliveira
\n\n\ndimitreoliveira used a augmentations to improve the flowers model's performance.
\n
If you want to become an expert in the field of computer vision, then you need to go through dimitreoliveira notebook. This notebook will teach you how to train a deep learning model that can be used to classify objects in pictures. Not only that, but you will also learn how to improve the accuracy of your predictions.
\nPytorch Tpu Baseline Flowers Tranlearning Ensemble by georgiisirotenko
\n\n\ngeorgiisirotenko trained multiple neural networks to recognize flowers and then combined their predictions together to create a more accurate final prediction.
\n
If you're looking to get into deep learning, and want to try something a little different, I'd definitely recommend checking out this notebook. georgiisirotenko uses a clever approach to improving accuracy.
\nDifferential Evolution by allunia
\n\n\nDifferential-Evolution is a technique that helps evolve better solutions to problems.
\n
This notebook is an exploration of the differential evolution (DE) algorithm for optimization. DE is a mutation-based, population-based evolutionary algorithm that has been shown to be very effective in solving a wide range of problems. The examples in this notebook show how you can use DE to optimize a variety of things, from simple linear functions to more complex functions and problems.
\nDetailed Guide To Custom Training With Tpus by yihdarshieh
\n\n\nyihdarshieh showed how to do a custom training with TPUs and how to use data augmentation.
\n
This notebook provides a detailed guide on how to do custom training with TPUs. You will learn about data pipelines, distributed datasets, and how to use TPUs for training. You will also see some useful augmentation techniques that can be used to improve the quality of your data. Finally, you will perform some comparative training and evaluate the results. I hope you enjoy this notebook and learn something new!
\nMake Chris Deotte S Data Augmentation Faster by yihdarshieh
\n\n\nyihdarshieh used a special layer to make data augmentation faster.
\n
If you're looking to speed up your data augmentation workflows, look no further! yihdarshieh's notebook will show you how to do just that. Using some nifty tricks and techniques, you can easily speed up your data augmentation process by 2-3x. So don't spend anymore time twiddling your thumbs - get started with yihdarshieh's notebook today!
\nPretrained Cnn Epic Fight by servietsky
\n\n\nservietsky used different types of models to learn how to predict the different types of flowers.
\n
If you are ever looking for a notebook that is intense, fun, and full of learning experiences, then you must go through servietsky's notebook. In it, you will find deep insights into the workings of a CNN and how to apply it in a TensorFlow environment. servietsky has taken great care in providing clear explanations with accompanying code samples that are easy to follow. You will also be able to find interesting tips and tricks that can make your implementation easier than ever.
\nFlower Classification Augmentations Eda by nitindatta
\n\n\nnitindatta compared different flower augmentation techniques for image recognition in a flower dataset.
\n
This notebook contains a detailed account of the different augmentations that were applied to the flower-classification model. The various techniques have been evaluated and the results are presented in an easy-to-understand manner. The reader can get a clear idea of the performance of each method and can use this information to improve their own models.
", "messages": [ { "id": 1791203, "postDate": "2022-05-15T18:10:59.237Z", "content": "'
\nListen up, guys! These are the jupyter notebooks you'll need to start learning about the tpu-getting-started competition.
\nPay attention, because if you don't know how to use them by the time we're done, you are missing out!
Enjoy! <3
\nRotation Augmentation Gpu Tpu 0 96 by cdeotte
\n\n\ncdeotte used rotation augmentation on the TPU
\n
This notebook will show you how to improve the accuracy of a deep learning model using augmentation techniques. You will learn how to perform image rotations, zooming, and translation using TensorFlow's built-in transformations library. You will also learn how to use a GPU or TPU for training your models with improved speed and accuracy.
\nCutmix And Mixup On Gpu Tpu by cdeotte
\n\n\nThis notebook demonstrates how to use the cut-mix-and-mixup algorithm on a GPU or TPU.
\n
Same as before just with different augmentation methods. To get the best performance results, it is often recommended to use a GPU or TPU. However, if you are limited to a CPU, this notebook will show you how to get good results with a cut-mix-and-mixup approach.
\nTfrecords Basics by ryanholbrook
\n\n\nTfrecords-Basics: This notebook explains how to read and write data in the TFRecord format.
\n
tfrecords is a powerful way to save and load data in TensorFlow. This notebook will walk you through the basics of working with tfrecords files, including how to create them, how to load them, and how to use them with TensorFlow.
\nComputer Vision Petals To The Metal by georgezoto
\n\n\nThis notebook contains code that allows you to submit predictions for a computer vision competition.
\n
If you want to learn how to solve a computer vision problem, this is the notebook for you. georgezoto walks you through every step of the process, from loading the data to making submissions to the competition leaderboard. There's also plenty of room for improvement - so don't stop at just one submission! georgezoto's notebook will help you climb up the leaderboard and learn some essential skills in the process.
\nFc Ensemble External Data Effnet Densenet by atamazian
\n\n\natamazian combined a lot of different models to make a better model that predict better.
\n
If you are looking to train a deep learning model on a large dataset, and want to ensure good accuracy without spending too much time on tuning hyperparameters, then ensemble learning is a great option. In this notebook, we walk through an example of using two deep neural networks models (densenet and effneet), to achieve good predictive accuracy.
\nFlower Classification With Tpus Eda And Baseline by dimitreoliveira
\n\n\ndimitreoliveira created a baseline on the TPU to help identify flowers using.
\n
If you're looking to push the boundaries of machine learning and deep learning, dimitreoliveira's notebook is the perfect place to start. Featuring a state-of-the-art TensorFlow implementation, this notebook will show you how to train a model on a real-world dataset. You'll also learn how to evaluate your model and make predictions on new data.
\nJax Flax Tf Data Vision Transformers Tutorial by odins0n
\n\n\nodins0n shows us how to use Tf-Data, Jax and Flax to do predictions on images.
\n
If you are looking to get started with deep learning and image data, this is a great tutorial to get started. You will learn how to use the Jax-Flax library as well as the TensorFlow data pipeline. You will also learn how to use the Vision Transformers library to improve your predictions.
\nA Simple Petals TF2 Notebook by philculliton
\n\n\nphilculliton used the code provided to train a TF2 model on a set of images.
\n
This notebook guides you through training a simple convolutional neural network for classifying flowers using the TensorFlow 2.1 API. You'll learn how to construct a model, train it on data, and evaluate its performance. The code examples are rich and easy to follow, making this an excellent resource for anyone looking to get started with deep learning and TensorFlow 2.1.
\nTf Hybrid Efficientnet Swin Transformer Gradcam by ipythonx
\n\n\nipythonx used a model called Swin combined with efficientnet to create an ensemble.
\n
This is one great notebook on using TensorFlow, the Hybrid-Efficientnet, and Swin Transformer for gradcam. ipythonx has clearly taken a lot of time and effort in putting this together, and it is definitely worth going through. The code is explained very well, and it's easy to follow along. You'll learn a lot by going through this notebook!
\nFlower With Tpus Advanced Augmentations by dimitreoliveira
\n\n\ndimitreoliveira used a augmentations to improve the flowers model's performance.
\n
If you want to become an expert in the field of computer vision, then you need to go through dimitreoliveira notebook. This notebook will teach you how to train a deep learning model that can be used to classify objects in pictures. Not only that, but you will also learn how to improve the accuracy of your predictions.
\nPytorch Tpu Baseline Flowers Tranlearning Ensemble by georgiisirotenko
\n\n\ngeorgiisirotenko trained multiple neural networks to recognize flowers and then combined their predictions together to create a more accurate final prediction.
\n
If you're looking to get into deep learning, and want to try something a little different, I'd definitely recommend checking out this notebook. georgiisirotenko uses a clever approach to improving accuracy.
\nDifferential Evolution by allunia
\n\n\nDifferential-Evolution is a technique that helps evolve better solutions to problems.
\n
This notebook is an exploration of the differential evolution (DE) algorithm for optimization. DE is a mutation-based, population-based evolutionary algorithm that has been shown to be very effective in solving a wide range of problems. The examples in this notebook show how you can use DE to optimize a variety of things, from simple linear functions to more complex functions and problems.
\nDetailed Guide To Custom Training With Tpus by yihdarshieh
\n\n\nyihdarshieh showed how to do a custom training with TPUs and how to use data augmentation.
\n
This notebook provides a detailed guide on how to do custom training with TPUs. You will learn about data pipelines, distributed datasets, and how to use TPUs for training. You will also see some useful augmentation techniques that can be used to improve the quality of your data. Finally, you will perform some comparative training and evaluate the results. I hope you enjoy this notebook and learn something new!
\nMake Chris Deotte S Data Augmentation Faster by yihdarshieh
\n\n\nyihdarshieh used a special layer to make data augmentation faster.
\n
If you're looking to speed up your data augmentation workflows, look no further! yihdarshieh's notebook will show you how to do just that. Using some nifty tricks and techniques, you can easily speed up your data augmentation process by 2-3x. So don't spend anymore time twiddling your thumbs - get started with yihdarshieh's notebook today!
\nPretrained Cnn Epic Fight by servietsky
\n\n\nservietsky used different types of models to learn how to predict the different types of flowers.
\n
If you are ever looking for a notebook that is intense, fun, and full of learning experiences, then you must go through servietsky's notebook. In it, you will find deep insights into the workings of a CNN and how to apply it in a TensorFlow environment. servietsky has taken great care in providing clear explanations with accompanying code samples that are easy to follow. You will also be able to find interesting tips and tricks that can make your implementation easier than ever.
\nFlower Classification Augmentations Eda by nitindatta
\n\n\nnitindatta compared different flower augmentation techniques for image recognition in a flower dataset.
\n
This notebook contains a detailed account of the different augmentations that were applied to the flower-classification model. The various techniques have been evaluated and the results are presented in an easy-to-understand manner. The reader can get a clear idea of the performance of each method and can use this information to improve their own models.
", "rawMarkdown": "'\n\n\nListen up, guys! These are the jupyter notebooks you'll need to start learning about the tpu-getting-started competition.\nPay attention, because if you don't know how to use them by the time we're done, you are missing out!\n\nEnjoy! <3\n_____\n\n\n**[Rotation Augmentation Gpu Tpu 0 96](https://kaggle.com/cdeotte/rotation-augmentation-gpu-tpu-0-96) by cdeotte**\n> cdeotte used rotation augmentation on the TPU\n\nThis notebook will show you how to improve the accuracy of a deep learning model using augmentation techniques. You will learn how to perform image rotations, zooming, and translation using TensorFlow's built-in transformations library. You will also learn how to use a GPU or TPU for training your models with improved speed and accuracy.\n\n\n\n_____\n\n\n**[Cutmix And Mixup On Gpu Tpu](https://kaggle.com/cdeotte/cutmix-and-mixup-on-gpu-tpu) by cdeotte**\n> This notebook demonstrates how to use the cut-mix-and-mixup algorithm on a GPU or TPU.\n\nSame as before just with different augmentation methods. To get the best performance results, it is often recommended to use a GPU or TPU. However, if you are limited to a CPU, this notebook will show you how to get good results with a cut-mix-and-mixup approach.\n\n\n_____\n\n\n**[Tfrecords Basics](https://kaggle.com/ryanholbrook/tfrecords-basics) by ryanholbrook**\n> Tfrecords-Basics: This notebook explains how to read and write data in the TFRecord format.\n\ntfrecords is a powerful way to save and load data in TensorFlow. This notebook will walk you through the basics of working with tfrecords files, including how to create them, how to load them, and how to use them with TensorFlow.\n\n\n\n_____\n\n\n**[Computer Vision Petals To The Metal](https://kaggle.com/georgezoto/computer-vision-petals-to-the-metal) by georgezoto**\n> This notebook contains code that allows you to submit predictions for a computer vision competition.\n\nIf you want to learn how to solve a computer vision problem, this is the notebook for you. georgezoto walks you through every step of the process, from loading the data to making submissions to the competition leaderboard. There's also plenty of room for improvement - so don't stop at just one submission! georgezoto's notebook will help you climb up the leaderboard and learn some essential skills in the process.\n\n\n\n_____\n\n\n**[Fc Ensemble External Data Effnet Densenet](https://kaggle.com/atamazian/fc-ensemble-external-data-effnet-densenet) by atamazian**\n> atamazian combined a lot of different models to make a better model that predict better.\n\nIf you are looking to train a deep learning model on a large dataset, and want to ensure good accuracy without spending too much time on tuning hyperparameters, then ensemble learning is a great option. In this notebook, we walk through an example of using two deep neural networks models (densenet and effneet), to achieve good predictive accuracy.\n\n\n\n_____\n\n\n**[Flower Classification With Tpus Eda And Baseline](https://kaggle.com/dimitreoliveira/flower-classification-with-tpus-eda-and-baseline) by dimitreoliveira**\n> dimitreoliveira created a baseline on the TPU to help identify flowers using.\n\nIf you're looking to push the boundaries of machine learning and deep learning, dimitreoliveira's notebook is the perfect place to start. Featuring a state-of-the-art TensorFlow implementation, this notebook will show you how to train a model on a real-world dataset. You'll also learn how to evaluate your model and make predictions on new data.\n\n\n\n_____\n\n\n**[Jax Flax Tf Data Vision Transformers Tutorial](https://kaggle.com/odins0n/jax-flax-tf-data-vision-transformers-tutorial) by odins0n**\n> odins0n shows us how to use Tf-Data, Jax and Flax to do predictions on images.\n\nIf you are looking to get started with deep learning and image data, this is a great tutorial to get started. You will learn how to use the Jax-Flax library as well as the TensorFlow data pipeline. You will also learn how to use the Vision Transformers library to improve your predictions.\n\n\n\n_____\n\n\n**[A Simple Petals TF2 Notebook](https://kaggle.com/philculliton/a-simple-petals-tf-2-2-notebook) by philculliton**\n> philculliton used the code provided to train a TF2 model on a set of images.\n\nThis notebook guides you through training a simple convolutional neural network for classifying flowers using the TensorFlow 2.1 API. You'll learn how to construct a model, train it on data, and evaluate its performance. The code examples are rich and easy to follow, making this an excellent resource for anyone looking to get started with deep learning and TensorFlow 2.1.\n\n\n\n_____\n\n\n**[Tf Hybrid Efficientnet Swin Transformer Gradcam](https://kaggle.com/ipythonx/tf-hybrid-efficientnet-swin-transformer-gradcam) by ipythonx**\n> ipythonx used a model called **Swin** combined with efficientnet to create an ensemble.\n\nThis is one great notebook on using TensorFlow, the Hybrid-Efficientnet, and Swin Transformer for gradcam. ipythonx has clearly taken a lot of time and effort in putting this together, and it is definitely worth going through. The code is explained very well, and it's easy to follow along. You'll learn a lot by going through this notebook!\n\n\n\n_____\n\n\n**[Flower With Tpus Advanced Augmentations](https://kaggle.com/dimitreoliveira/flower-with-tpus-advanced-augmentations) by dimitreoliveira**\n> dimitreoliveira used a augmentations to improve the flowers model's performance.\n\nIf you want to become an expert in the field of computer vision, then you need to go through dimitreoliveira notebook. This notebook will teach you how to train a deep learning model that can be used to classify objects in pictures. Not only that, but you will also learn how to improve the accuracy of your predictions.\n\n\n\n_____\n\n\n**[Pytorch Tpu Baseline Flowers Tranlearning Ensemble](https://kaggle.com/georgiisirotenko/pytorch-tpu-baseline-flowers-tranlearning-ensemble) by georgiisirotenko**\n> georgiisirotenko trained multiple neural networks to recognize flowers and then combined their predictions together to create a more accurate final prediction.\n\nIf you're looking to get into deep learning, and want to try something a little different, I'd definitely recommend checking out this notebook. georgiisirotenko uses a clever approach to improving accuracy.\n\n\n\n_____\n\n\n**[Differential Evolution](https://kaggle.com/allunia/differential-evolution) by allunia**\n> Differential-Evolution is a technique that helps evolve better solutions to problems.\n\nThis notebook is an exploration of the differential evolution (DE) algorithm for optimization. DE is a mutation-based, population-based evolutionary algorithm that has been shown to be very effective in solving a wide range of problems. The examples in this notebook show how you can use DE to optimize a variety of things, from simple linear functions to more complex functions and problems.\n\n\n\n_____\n\n\n**[Detailed Guide To Custom Training With Tpus](https://kaggle.com/yihdarshieh/detailed-guide-to-custom-training-with-tpus) by yihdarshieh**\n> yihdarshieh showed how to do a custom training with TPUs and how to use data augmentation.\n\nThis notebook provides a detailed guide on how to do custom training with TPUs. You will learn about data pipelines, distributed datasets, and how to use TPUs for training. You will also see some useful augmentation techniques that can be used to improve the quality of your data. Finally, you will perform some comparative training and evaluate the results. I hope you enjoy this notebook and learn something new!\n\n\n\n_____\n\n\n**[Make Chris Deotte S Data Augmentation Faster](https://kaggle.com/yihdarshieh/make-chris-deotte-s-data-augmentation-faster) by yihdarshieh**\n> yihdarshieh used a special layer to make data augmentation faster.\n\nIf you're looking to speed up your data augmentation workflows, look no further! yihdarshieh's notebook will show you how to do just that. Using some nifty tricks and techniques, you can easily speed up your data augmentation process by 2-3x. So don't spend anymore time twiddling your thumbs - get started with yihdarshieh's notebook today!\n\n\n\n_____\n\n\n**[Pretrained Cnn Epic Fight](https://kaggle.com/servietsky/pretrained-cnn-epic-fight) by servietsky**\n> servietsky used different types of models to learn how to predict the different types of flowers.\n\nIf you are ever looking for a notebook that is intense, fun, and full of learning experiences, then you must go through servietsky's notebook. In it, you will find deep insights into the workings of a CNN and how to apply it in a TensorFlow environment. servietsky has taken great care in providing clear explanations with accompanying code samples that are easy to follow. You will also be able to find interesting tips and tricks that can make your implementation easier than ever.\n\n\n\n_____\n\n\n**[Flower Classification Augmentations Eda](https://kaggle.com/nitindatta/flower-classification-augmentations-eda) by nitindatta**\n> nitindatta compared different flower augmentation techniques for image recognition in a flower dataset.\n\nThis notebook contains a detailed account of the different augmentations that were applied to the flower-classification model. The various techniques have been evaluated and the results are presented in an easy-to-understand manner. The reader can get a clear idea of the performance of each method and can use this information to improve their own models.\n\n\n", "votes": 5 }, { "id": 1791238, "postDate": "2022-05-15T19:04:42.820Z", "content": "@satoshidatamoto Thank you for sharing, I have a lot to learn from this. Never dealt with TPU before
", "rawMarkdown": "@satoshidatamoto Thank you for sharing, I have a lot to learn from this. Never dealt with TPU before", "votes": 1 }, { "id": 2428493, "postDate": "2023-09-07T23:38:07.407Z", "content": "This is really helpful today i have learned so any new techniques also it helped me to understand the basis of each technique used and their purpose
", "rawMarkdown": "This is really helpful today i have learned so any new techniques also it helped me to understand the basis of each technique used and their purpose" }, { "id": 1805209, "postDate": "2022-05-29T23:24:43.517Z", "content": "thank you for sharing
", "rawMarkdown": "thank you for sharing\n", "votes": 1 } ], "comments": [ { "id": 1791238, "author_name": "💥Alien💥", "author_url": "", "post_date": "2022-05-15T19:04:42.820000", "content": "@satoshidatamoto Thank you for sharing, I have a lot to learn from this. Never dealt with TPU before
", "votes": 1, "replies": [] }, { "id": 2428493, "author_name": "Pratik Singh bharadwaj", "author_url": "", "post_date": "2023-09-07T23:38:07.407000", "content": "This is really helpful today i have learned so any new techniques also it helped me to understand the basis of each technique used and their purpose
", "votes": 0, "replies": [] }, { "id": 1805209, "author_name": "mohammed youcef somaa", "author_url": "", "post_date": "2022-05-29T23:24:43.517000", "content": "thank you for sharing
", "votes": 1, "replies": [] } ], "raw_markdown_by_id": { "1791203": "'\n\n\nListen up, guys! These are the jupyter notebooks you'll need to start learning about the tpu-getting-started competition.\nPay attention, because if you don't know how to use them by the time we're done, you are missing out!\n\nEnjoy! <3\n_____\n\n\n**[Rotation Augmentation Gpu Tpu 0 96](https://kaggle.com/cdeotte/rotation-augmentation-gpu-tpu-0-96) by cdeotte**\n> cdeotte used rotation augmentation on the TPU\n\nThis notebook will show you how to improve the accuracy of a deep learning model using augmentation techniques. You will learn how to perform image rotations, zooming, and translation using TensorFlow's built-in transformations library. You will also learn how to use a GPU or TPU for training your models with improved speed and accuracy.\n\n\n\n_____\n\n\n**[Cutmix And Mixup On Gpu Tpu](https://kaggle.com/cdeotte/cutmix-and-mixup-on-gpu-tpu) by cdeotte**\n> This notebook demonstrates how to use the cut-mix-and-mixup algorithm on a GPU or TPU.\n\nSame as before just with different augmentation methods. To get the best performance results, it is often recommended to use a GPU or TPU. However, if you are limited to a CPU, this notebook will show you how to get good results with a cut-mix-and-mixup approach.\n\n\n_____\n\n\n**[Tfrecords Basics](https://kaggle.com/ryanholbrook/tfrecords-basics) by ryanholbrook**\n> Tfrecords-Basics: This notebook explains how to read and write data in the TFRecord format.\n\ntfrecords is a powerful way to save and load data in TensorFlow. This notebook will walk you through the basics of working with tfrecords files, including how to create them, how to load them, and how to use them with TensorFlow.\n\n\n\n_____\n\n\n**[Computer Vision Petals To The Metal](https://kaggle.com/georgezoto/computer-vision-petals-to-the-metal) by georgezoto**\n> This notebook contains code that allows you to submit predictions for a computer vision competition.\n\nIf you want to learn how to solve a computer vision problem, this is the notebook for you. georgezoto walks you through every step of the process, from loading the data to making submissions to the competition leaderboard. There's also plenty of room for improvement - so don't stop at just one submission! georgezoto's notebook will help you climb up the leaderboard and learn some essential skills in the process.\n\n\n\n_____\n\n\n**[Fc Ensemble External Data Effnet Densenet](https://kaggle.com/atamazian/fc-ensemble-external-data-effnet-densenet) by atamazian**\n> atamazian combined a lot of different models to make a better model that predict better.\n\nIf you are looking to train a deep learning model on a large dataset, and want to ensure good accuracy without spending too much time on tuning hyperparameters, then ensemble learning is a great option. In this notebook, we walk through an example of using two deep neural networks models (densenet and effneet), to achieve good predictive accuracy.\n\n\n\n_____\n\n\n**[Flower Classification With Tpus Eda And Baseline](https://kaggle.com/dimitreoliveira/flower-classification-with-tpus-eda-and-baseline) by dimitreoliveira**\n> dimitreoliveira created a baseline on the TPU to help identify flowers using.\n\nIf you're looking to push the boundaries of machine learning and deep learning, dimitreoliveira's notebook is the perfect place to start. Featuring a state-of-the-art TensorFlow implementation, this notebook will show you how to train a model on a real-world dataset. You'll also learn how to evaluate your model and make predictions on new data.\n\n\n\n_____\n\n\n**[Jax Flax Tf Data Vision Transformers Tutorial](https://kaggle.com/odins0n/jax-flax-tf-data-vision-transformers-tutorial) by odins0n**\n> odins0n shows us how to use Tf-Data, Jax and Flax to do predictions on images.\n\nIf you are looking to get started with deep learning and image data, this is a great tutorial to get started. You will learn how to use the Jax-Flax library as well as the TensorFlow data pipeline. You will also learn how to use the Vision Transformers library to improve your predictions.\n\n\n\n_____\n\n\n**[A Simple Petals TF2 Notebook](https://kaggle.com/philculliton/a-simple-petals-tf-2-2-notebook) by philculliton**\n> philculliton used the code provided to train a TF2 model on a set of images.\n\nThis notebook guides you through training a simple convolutional neural network for classifying flowers using the TensorFlow 2.1 API. You'll learn how to construct a model, train it on data, and evaluate its performance. The code examples are rich and easy to follow, making this an excellent resource for anyone looking to get started with deep learning and TensorFlow 2.1.\n\n\n\n_____\n\n\n**[Tf Hybrid Efficientnet Swin Transformer Gradcam](https://kaggle.com/ipythonx/tf-hybrid-efficientnet-swin-transformer-gradcam) by ipythonx**\n> ipythonx used a model called **Swin** combined with efficientnet to create an ensemble.\n\nThis is one great notebook on using TensorFlow, the Hybrid-Efficientnet, and Swin Transformer for gradcam. ipythonx has clearly taken a lot of time and effort in putting this together, and it is definitely worth going through. The code is explained very well, and it's easy to follow along. You'll learn a lot by going through this notebook!\n\n\n\n_____\n\n\n**[Flower With Tpus Advanced Augmentations](https://kaggle.com/dimitreoliveira/flower-with-tpus-advanced-augmentations) by dimitreoliveira**\n> dimitreoliveira used a augmentations to improve the flowers model's performance.\n\nIf you want to become an expert in the field of computer vision, then you need to go through dimitreoliveira notebook. This notebook will teach you how to train a deep learning model that can be used to classify objects in pictures. Not only that, but you will also learn how to improve the accuracy of your predictions.\n\n\n\n_____\n\n\n**[Pytorch Tpu Baseline Flowers Tranlearning Ensemble](https://kaggle.com/georgiisirotenko/pytorch-tpu-baseline-flowers-tranlearning-ensemble) by georgiisirotenko**\n> georgiisirotenko trained multiple neural networks to recognize flowers and then combined their predictions together to create a more accurate final prediction.\n\nIf you're looking to get into deep learning, and want to try something a little different, I'd definitely recommend checking out this notebook. georgiisirotenko uses a clever approach to improving accuracy.\n\n\n\n_____\n\n\n**[Differential Evolution](https://kaggle.com/allunia/differential-evolution) by allunia**\n> Differential-Evolution is a technique that helps evolve better solutions to problems.\n\nThis notebook is an exploration of the differential evolution (DE) algorithm for optimization. DE is a mutation-based, population-based evolutionary algorithm that has been shown to be very effective in solving a wide range of problems. The examples in this notebook show how you can use DE to optimize a variety of things, from simple linear functions to more complex functions and problems.\n\n\n\n_____\n\n\n**[Detailed Guide To Custom Training With Tpus](https://kaggle.com/yihdarshieh/detailed-guide-to-custom-training-with-tpus) by yihdarshieh**\n> yihdarshieh showed how to do a custom training with TPUs and how to use data augmentation.\n\nThis notebook provides a detailed guide on how to do custom training with TPUs. You will learn about data pipelines, distributed datasets, and how to use TPUs for training. You will also see some useful augmentation techniques that can be used to improve the quality of your data. Finally, you will perform some comparative training and evaluate the results. I hope you enjoy this notebook and learn something new!\n\n\n\n_____\n\n\n**[Make Chris Deotte S Data Augmentation Faster](https://kaggle.com/yihdarshieh/make-chris-deotte-s-data-augmentation-faster) by yihdarshieh**\n> yihdarshieh used a special layer to make data augmentation faster.\n\nIf you're looking to speed up your data augmentation workflows, look no further! yihdarshieh's notebook will show you how to do just that. Using some nifty tricks and techniques, you can easily speed up your data augmentation process by 2-3x. So don't spend anymore time twiddling your thumbs - get started with yihdarshieh's notebook today!\n\n\n\n_____\n\n\n**[Pretrained Cnn Epic Fight](https://kaggle.com/servietsky/pretrained-cnn-epic-fight) by servietsky**\n> servietsky used different types of models to learn how to predict the different types of flowers.\n\nIf you are ever looking for a notebook that is intense, fun, and full of learning experiences, then you must go through servietsky's notebook. In it, you will find deep insights into the workings of a CNN and how to apply it in a TensorFlow environment. servietsky has taken great care in providing clear explanations with accompanying code samples that are easy to follow. You will also be able to find interesting tips and tricks that can make your implementation easier than ever.\n\n\n\n_____\n\n\n**[Flower Classification Augmentations Eda](https://kaggle.com/nitindatta/flower-classification-augmentations-eda) by nitindatta**\n> nitindatta compared different flower augmentation techniques for image recognition in a flower dataset.\n\nThis notebook contains a detailed account of the different augmentations that were applied to the flower-classification model. The various techniques have been evaluated and the results are presented in an easy-to-understand manner. The reader can get a clear idea of the performance of each method and can use this information to improve their own models.\n\n\n", "1791238": "@satoshidatamoto Thank you for sharing, I have a lot to learn from this. Never dealt with TPU before", "2428493": "This is really helpful today i have learned so any new techniques also it helped me to understand the basis of each technique used and their purpose", "1805209": "thank you for sharing\n" } }