- ID da verificação
- c7942b3d-016e-4b12-9946-3b5a2b8216f5Concluído
- URL enviado:
- https://tensorflow-training.org/
- Relatório concluído:
Links · 3 encontrado(s)
Os links de saída identificados na página
| Link | Texto |
|---|---|
| https://www.tensorflow.org/install | Install |
| https://tensorflow.training/lms/login/index.php | Sign In |
| https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/quickstart/beginner.ipynb | Run quickstart |
Variáveis JavaScript · 59 encontrada(s)
Variáveis JavaScript globais carregadas no objeto janela de uma página são variáveis declaradas fora das funções e acessíveis de qualquer lugar no código dentro do escopo atual
| Nome | Tipo |
|---|---|
| 0 | object |
| 1 | object |
| 2 | object |
| 3 | object |
| onbeforetoggle | object |
| documentPictureInPicture | object |
| onscrollend | object |
| $ | function |
| jQuery | function |
| bootstrap | object |
Mensagens de registro do console · 2 encontrada(s)
Mensagens registradas no console web
| Tipo | Categoria | Log |
|---|---|---|
| error | network |
|
| error | network |
|
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<h5 class="h5-lg"><i class="bi bi-star-fill"></i> 186k TF2.17 released</h5>
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<p class="pt-3"><span style="color:darkblue">import</span> <span style="color: purple">tensorflow</span> <span style="color:darkblue">as</span> <span style="color: purple">tf</span></p>
<p>mnist = tf.keras.datasets.mnist</p>
<p>
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / <span style="color: maroon;">255.0</span>, x_test /
<span style="color: maroon;">255.0</span></p>
<p>model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(<span style="color: maroon;">28</span>,
<span style="color: maroon;">28</span>)),
tf.keras.layers.Dense(<span style="color: maroon;">128</span>, activation='<span style="color: green;">relu</span>'),
tf.keras.layers.Dropout(<span style="color: maroon;">0.2</span>),
<br>
tf.keras.layers.Dense(<span style="color: maroon;">
10
</span>, activation='softmax')
<br>
])
</p>
<p>model.compile(optimizer='adam',
loss=' <span style="color: green;"> sparse_categorical_crossentropy</span>',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)
</p>
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<span style="font-weight: bold;">TENSORFLOW GNN</span>
<!-- <span>English</span> -->
</p>
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<h5 class="h5-xs"> Analyze relational data using graph neural networks</h5>
<p style="font-weight: 400;">
GNNs can process complex relationships between objects, making them a powerful
technique for traffic forecasting, medical discovery, and more.
</p>
</div>
</a>
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<span style="font-weight: bold;"> TENSORFIOW LITE</span>
</p>
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<h5 class="h5-xs"> Improving access to maternal health with on-device ML</h5>
<p style="font-weight: 400;">Learn how TensorFlow Lite enables access to fetal
ultrasound assessment, improving health outcomes for women and families around
Kenya and the world.</p>
<!-- Course Price -->
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</a>
</div>
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<span style="font-weight: bold;">TENSORFLOW AGENTS</span>
</p>
<!-- Title -->
<h5 class="h5-xs">Build recommendation systems with reinforcement learning</h5>
<p style="font-weight: 400;">Learn how Spotify uses the TensorFlow ecosystem to
design an extendable offline simulator and train RL Agents to generate
playlists.</p>
<!-- Course Rating -->
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<i class="fas fa-star"></i>
<i class="fas fa-star"></i>
<i class="fas fa-star-half-alt"></i>
<span>4.5 (72 Ratings)</span>
</div> -->
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</div>
</div>
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</section>
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============================================= -->
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<div class="container">
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<div class="row">
<div class="col-md-12">
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<!-- Text -->
<p class="p-md" style="font-weight: 500;">Discover production-tested tools to accelerate
modeling, deployment, and other workflows.
</p>
</div>
</div>
</div>
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<div class="row">
<div class="col-md-12">
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<!--END TESTIMONIAL #1 -->
<div class="owl-stage-outer"><div class="owl-stage" style="transform: translate3d(-1380px, 0px, 0px); transition: all 0s ease 0s; width: 5520px;"><div class="owl-item cloned" style="width: 345px;"><div class="review-1" style="width: 350px;">
<!-- Quote Icon -->
<!-- <div class="quote-ico"><img src="images/left-quote.png" alt="quote-image" /></div> -->
<p class="t"> API</p>
<p class="ti"> tf.keras</p>
<!-- Testimonial Text -->
<p> Create ML models with TensorFlow's high-level API.
</p>
<!-- Author Data -->
</div></div><div class="owl-item cloned" style="width: 345px;"><div class="review-1" style="width: 350px;">
<!-- Quote Icon -->
<!-- <div class="quote-ico"><img src="images/left-quote.png" alt="quote-image" /></div> -->
<p class="t"> Resource</p>
<p class="ti"> Kaggle Models</p>
<!-- Testimonial Text -->
<p> Find pre-trained models ready for fine-tuning and deployment.
</p>
<!-- Author Data -->
</div></div><div class="owl-item cloned" style="width: 345px;"><div class="review-1" style="width: 350px;">
<!-- Quote Icon -->
<!-- <div class="quote-ico"><img src="images/left-quote.png" alt="quote-image" /></div> -->
<p class="t"> Resource</p>
<p class="ti"> TensorFlow Datasets</p>
<!-- Testimonial Text -->
<p>Browse the collection of standard datasets for initial training and validation.
</p>
<!-- Author Data -->
</div></div><div class="owl-item cloned" style="width: 345px;"><div class="review-1" style="width: 350px;">
<!-- Quote Icon -->
<!-- <div class="quote-ico"><img src="images/left-quote.png" alt="quote-image" /></div> -->
<p class="t"> Tool</p>
<p class="ti"> TensorBoard</p>
<!-- Testimonial Text -->
<p> Visualize and track development of ML models.
</p>
<!-- Author Data -->
</div></div><div class="owl-item active" style="width: 345px;"><div class="review-1" style="width: 350px;">
<!-- Quote Icon -->
<!-- <div class="quote-ico"><img src="images/left-quote.png" alt="quote-image" /></div> -->
<p class="t">LIBRARY</p>
<p class="ti">TensorFlow Lite</p>
<!-- Testimonial Text -->
<p> Deploy ML on mobile and edge devices such as Android, iOS, Raspberry Pi, and Edge
TPU.
</p>
<!-- Author Data -->
</div></div><div class="owl-item active" style="width: 345px;"><div class="review-1" style="width: 350px;">
<!-- Quote Icon -->
<!-- <div class="quote-ico"><img src="images/left-quote.png" alt="quote-image" /></div> -->
<p class="t">LIBRARY</p>
<p class="ti"> TensorFlow.js</p>
<!-- Testimonial Text -->
<p> Train and run models directly in the browser using JavaScript or Node.js.
</p>
<!-- Author Data -->
</div></div><div class="owl-item" style="width: 345px;"><div class="review-1" style="width: 350px;">
<!-- Quote Icon -->
<!-- <div class="quote-ico"><img src="images/left-quote.png" alt="quote-image" /></div> -->
<p class="t"> API</p>
<p class="ti"> tf.data</p>
<!-- Testimonial Text -->
<p> Preprocess data and create input pipelines for ML models..
</p>
<!-- Author Data -->
</div></div><div class="owl-item" style="width: 345px;"><div class="review-1" style="width: 350px;">
<!-- Quote Icon -->
<!-- <div class="quote-ico"><img src="images/left-quote.png" alt="quote-image" /></div> -->
<p class="t"> Library</p>
<p class="ti"> TFX</p>
<!-- Testimonial Text -->
<p> Create production ML pipelines and implement MLOps best practices.
</p>
<!-- Author Data -->
</div></div><div class="owl-item" style="width: 345px;"><div class="review-1" style="width: 350px;">
<!-- Quote Icon -->
<!-- <div class="quote-ico"><img src="images/left-quote.png" alt="quote-image" /></div> -->
<p class="t"> API</p>
<p class="ti"> tf.keras</p>
<!-- Testimonial Text -->
<p> Create ML models with TensorFlow's high-level API.
</p>
<!-- Author Data -->
</div></div><div class="owl-item" style="width: 345px;"><div class="review-1" style="width: 350px;">
<!-- Quote Icon -->
<!-- <div class="quote-ico"><img src="images/left-quote.png" alt="quote-image" /></div> -->
<p class="t"> Resource</p>
<p class="ti"> Kaggle Models</p>
<!-- Testimonial Text -->
<p> Find pre-trained models ready for fine-tuning and deployment.
</p>
<!-- Author Data -->
</div></div><div class="owl-item" style="width: 345px;"><div class="review-1" style="width: 350px;">
<!-- Quote Icon -->
<!-- <div class="quote-ico"><img src="images/left-quote.png" alt="quote-image" /></div> -->
<p class="t"> Resource</p>
<p class="ti"> TensorFlow Datasets</p>
<!-- Testimonial Text -->
<p>Browse the collection of standard datasets for initial training and validation.
</p>
<!-- Author Data -->
</div></div><div class="owl-item" style="width: 345px;"><div class="review-1" style="width: 350px;">
<!-- Quote Icon -->
<!-- <div class="quote-ico"><img src="images/left-quote.png" alt="quote-image" /></div> -->
<p class="t"> Tool</p>
<p class="ti"> TensorBoard</p>
<!-- Testimonial Text -->
<p> Visualize and track development of ML models.
</p>
<!-- Author Data -->
</div></div><div class="owl-item cloned" style="width: 345px;"><div class="review-1" style="width: 350px;">
<!-- Quote Icon -->
<!-- <div class="quote-ico"><img src="images/left-quote.png" alt="quote-image" /></div> -->
<p class="t">LIBRARY</p>
<p class="ti">TensorFlow Lite</p>
<!-- Testimonial Text -->
<p> Deploy ML on mobile and edge devices such as Android, iOS, Raspberry Pi, and Edge
TPU.
</p>
<!-- Author Data -->
</div></div><div class="owl-item cloned" style="width: 345px;"><div class="review-1" style="width: 350px;">
<!-- Quote Icon -->
<!-- <div class="quote-ico"><img src="images/left-quote.png" alt="quote-image" /></div> -->
<p class="t">LIBRARY</p>
<p class="ti"> TensorFlow.js</p>
<!-- Testimonial Text -->
<p> Train and run models directly in the browser using JavaScript or Node.js.
</p>
<!-- Author Data -->
</div></div><div class="owl-item cloned" style="width: 345px;"><div class="review-1" style="width: 350px;">
<!-- Quote Icon -->
<!-- <div class="quote-ico"><img src="images/left-quote.png" alt="quote-image" /></div> -->
<p class="t"> API</p>
<p class="ti"> tf.data</p>
<!-- Testimonial Text -->
<p> Preprocess data and create input pipelines for ML models..
</p>
<!-- Author Data -->
</div></div><div class="owl-item cloned" style="width: 345px;"><div class="review-1" style="width: 350px;">
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