https://tensorflow-training.org/

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c7942b3d-016e-4b12-9946-3b5a2b8216f5Finalizado
URL enviada:
https://tensorflow-training.org/
Informe finalizado:

Enlaces: 3 encontrados

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EnlaceTexto
https://www.tensorflow.org/installInstall
https://tensorflow.training/lms/login/index.phpSign In
https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/quickstart/beginner.ipynbRun quickstart

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							<h5 class="h5-lg"><i class="bi bi-star-fill"></i> 186k TF2.17 released</h5>

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							<h3>An end-to-end platform for machine learning</h3>

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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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									<h6><a href="https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/quickstart/beginner.ipynb">Run
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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>

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										GNNs can process complex relationships between objects, making them a powerful
										technique for traffic forecasting, medical discovery, and more.


									</p>


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										<span style="font-weight: bold;"> TENSORFIOW LITE</span>
									</p>

									<!-- Title -->
									<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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										<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>
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											<span>4.5 (72 Ratings)</span>
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		</section>







	










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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>



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								<!-- 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 -->




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								<!-- 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 -->




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								<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 -->




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								<!-- 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 -->


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								<!-- 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 -->




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								<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 -->




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								<!-- 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 -->




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								<!-- 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 -->




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								<!-- 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.
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