Week 2.
Only thing remaining is How we know that How many element will be in each Hidden Layer and How do we know How many Hidden layers are required
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Hi @IRFANB146
Finding the number of neurons in each hidden layer and the number of hidden layers needed in a neural network architecture typically involves experimentation with different architectures and model performance evaluation.
There’s often no definitive answer, and it often requires iterative refinement based on empirical results. However, in some specific cases, you may find some rules of thumb that can be helpful.
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IS this the reason using Convolutional neural network is the best way as you distribute your input in different elements of first layer where you can define number of elements on first hidden layer
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The choice of neural network architecture depends on many things, like the nature of the data that we are training our models and the specific task. Convolutional Neural networks (CNNs) are particularly effective for tasks involving spatial data, such as image recognition. CNNs use convolutional layers to extract features from the input data in a spatially hierarchical manner starting from more complex to simpler shapes.
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