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Machine Learning, Scholarly, Tutorial
Building Neural Networks with Python Code and Math in Detail — II
The second part of our tutorial on neural networks from scratch. From the math behind them to step-by-step implementation case studies in Python. Launch the samples on Google Colab.
Last updated January 7, 2021
Author(s): Pratik Shukla, Roberto Iriondo
In the first part of our tutorial on neural networks, we explained the basic concepts about neural networks, from the math behind them to implementing neural networks in Python without any hidden layers. We showed how to make satisfactory predictions even in case scenarios where we did not use any hidden layers. However, there are several limitations to single-layer neural networks.
In this tutorial, we will dive in-depth into the limitations and advantages of using neural networks in machine learning. We will show how to implement neural nets with hidden layers and how these lead to a higher accuracy rate on our predictions, along with implementation samples in Python on Google Colab.
Index:
- Limitations and advantages of neural networks
- How to select several neurons in a hidden layer.
- The general structure of an artificial neural network (ANN).
- Implementation of a multilayer neural network in Python.
- Comparison with a single-layer neural network.
- Non-linearly separable data with a neural network.
- Conclusion.
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