Top 13 tensorflow-tutorial Open-Source Projects
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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tensorflow-deep-learning
All course materials for the Zero to Mastery Deep Learning with TensorFlow course. (by mrdbourke)
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Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials
A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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pocket-automl-android-tutorial
Pocket AutoML: Tutorial for Creating an Android App for Image Classification with Deep Learning
Project mention: Building an AI Game Bot 🤖Using Imitation Learning and 3D Convolution ResNet | dev.to | 2024-01-02def compute_mean_std(dataloader): ''' We assume that the images of the dataloader have the same height and width source: https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_std_mean.py ''' # var[X] = E[X**2] - E[X]**2 channels_sum, channels_sqrd_sum, num_batches = 0, 0, 0 for batch_images, labels in tqdm(dataloader): # (B,H,W,C) batch_images = batch_images.permute(0,3,4,2,1) channels_sum += torch.mean(batch_images, dim=[0, 1, 2, 3]) channels_sqrd_sum += torch.mean(batch_images ** 2, dim=[0, 1, 2,3]) num_batches += 1 mean = channels_sum / num_batches std = (channels_sqrd_sum / num_batches - mean ** 2) ** 0.5 return mean, std compute_mean_std(dataloader)
Project mention: Introducing FENfinder! Search across educational YouTube videos for specific chess positions. | /r/chess | 2023-04-21Basically, I download a video, split the video into images (1 img/sec), and then use a slightly modified version of chessfenbot to identify a chess position in the image. If I find one I store it in a database along with some metadata about the video where the position was seen.
After calling our components in HomeView.vue, we obtain the appearance in our visualization. If there are any differences or missing parts in your view, you can check TF.js-with-Vue3 for the repository. Alternatively, you can download it directly to your computer.
tensorflow-tutorials related posts
Index
What are some of the best open-source tensorflow-tutorial projects? This list will help you:
Project | Stars | |
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1 | TensorLayer | 7,275 |
2 | introtodeeplearning | 6,833 |
3 | Machine-Learning-Collection | 6,833 |
4 | docs | 6,011 |
5 | tensorflow-deep-learning | 4,849 |
6 | Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | 3,638 |
7 | yolov3-tf2 | 2,507 |
8 | awesome-colab-notebooks | 1,196 |
9 | tensorflow_chessbot | 496 |
10 | Gradient-Samples | 61 |
11 | TensorFlow2.0_Notebooks | 37 |
12 | pocket-automl-android-tutorial | 19 |
13 | TF.js-with-Vue3 | 0 |