Machine Learning Yearning refers to neural networks as also known as deep learning.

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Breaking this down into to components: - What are Neurons? - How are they connected? - At first, we can think view a neural network as how our brains work like neurons signaling one another (Also the inspiration for its name). In a sense these models allow computer programs to recognize patterns and solve common problems. - Neural Networks are comprised of node layers. It contains the input layer, one (or more) hidden layers, and an output layer. - Nodes (neurons) are connected via arrows and each has an associated weight and threshold. If the node surpasses the threshold set, then that neuron is activated and the data is sent on the next layer. This process repeats until it goes to the output layer.

Neural Network

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

Machine Learning Yearning (Deeplearning.ai)


While there are many types of neural networks the most common networks that will be typically seen are: 

- Feedforward neural networks
- Convolutional neural networks
- Recurrent neural networks

Types of Neural Networks

Python Neural Network Class

When working with Neural networks, it involves quite a bit a math to determine the weights and bias. This is so the computer can accurately determine the output. But in order to really figure that out. Fine tuning of the weights and bias from the input data is known as "training" the neural network.

Training Neural Networks

Neural Networks Also Known as Deep Learning

In this context, a small neural network means a neural network with only a small number of hidden units, layers, or parameters.

Small Neural Network Scale

Neural network architecture is one of the important details for performance, and there has been much innovation in neural network architecture.

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