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You are designing an Azure Machine Learning workflow.
You have a dataset that contains two million large digital photographs.
You plan to detect the presence of trees in the photographs.
You need to ensure that your model supports the following:
Hidden layers that support a directed graph structure
User-defined core components on the GPU
Solution: You create a Machine Learning experiment that implements the Multiclass Neural Network module.
Does this meet the goal?
A.
Yes
B.
No
Multiclass Neural Network
・Used to create a neural network model that can be used to predict a target that has multiple values.
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B.
Because Azure ML can not use GPU.
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Correct Answer is Yes.
A neural network is a set of interconnected layers. The inputs are the first layer, and are connected to an output layer by an acyclic graph comprised of weighted edges and nodes.
Between the input and output layers you can insert multiple hidden layers. Most predictive tasks can be accomplished easily with only one or a few hidden layers. However, recent research has shown that deep neural networks (DNN) with many layers can be very effective in complex tasks such as image or speech recognition. The successive layers are used to model increasing levels of semantic depth.
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Correction, Answer is NO (B)
– Only cognitive toolkit provides the feature to have user-defined core components of GPU
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Cognitive Tool Kit:>
a. Highly optimized, built-in components
b. Components can handle multi-dimensional dense or sparse data from Python, C++ or BrainScript
c. FFN, CNN, RNN/LSTM, Batch normalization, Sequence-to-Sequence with attention and more
d. Reinforcement learning, generative adversarial networks, supervised and unsupervised learning
e. Ability to add new user-defined core-components on the GPU from Python
f. Automatic hyperparameter tuning
Built-in readers optimized for massive datasets
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