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Tensor
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The primary data structure in TensorFlow programs. Tensors are N-dimensional (where N could be very large) data structures, most commonly scalars, vectors, or matrices. The elements of a Tensor can hold integer, floating-point, or string values.
Tensor Flow Course Master Page
See [[The Golem Project Overview]], the [[Main AI Page]], the [[Python - Main Page]], and the [[Master Contents Page]].
Also see the [[AI Glossary]] for conceptual help.
Tensor Flow 2.0: Deep Learning and Artificial Intelligence
The course material and videos are at Udemy, where the course is hosted.
Tensor Processing Unit (TPU)
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An application-specific integrated circuit (ASIC) that optimizes the performance of machine learning workloads. These ASICs are deployed as multiple TPU chips on a TPU device.
Tensor rank
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See rank (Tensor).
Tensor shape
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The number of elements a Tensor contains in various dimensions. For example, a [5, 10] Tensor has a shape of 5 in one dimension and 10 in another.
Tensor size
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The total number of scalars a Tensor contains. For example, a [5, 10] Tensor has a size of 50.
TensorBoard
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The dashboard that displays the summaries saved during the execution of one or more TensorFlow programs.
TensorFlow
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A large-scale, distributed, machine learning platform. The term also refers to the base API layer in the TensorFlow stack, which supports general computation on dataflow graphs.
Although TensorFlow is primarily used for machine learning, you may also use TensorFlow for non-ML tasks that require numerical computation using dataflow graphs.
See the [[Tensor Flow Course Master Page]]
TensorFlow Playground
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A program that visualizes how different hyperparameters influence model (primarily neural network) training. Go to http://playground.tensorflow.org to experiment with TensorFlow Playground.
TensorFlow Serving
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A platform to deploy trained models in production.
- public document at doc.anagora.org/tensor|tensor
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