**Pros:**
- They can effectively learn and capture highly complex, non-linear features given sufficient data and computational power.

**Cons:**
- Training time, required data, and customization efforts are significant if the model is large and complex.
- Input data must be carefully preprocessed and normalized before being passed to the network.

University of Michigan - Ann Arbor

Google

A helpful website that introduces neural networks:
https://missinglink.ai/guides/neural-network-concepts/

Neural Network Reference

https://techcrunch.com/2017/04/13/neural-networks-made-easy/

Neural network made easy

Depends greatly on the nature of the target task and domain, the availability of in-domain labels, the latency and capacity constraints of the applications, and so on. Has 5 steps.

How to choose the best neural network model for my task.

+ New Datasets for More Challenging Tasks
+ Modeling Commonsense Knowledge
+ Interpretable DL Models
+ Memory Efficient Models
+ Few-Shot and Zero-Shot Learning

Challenges and opportunities

5 main methods: Accuracy and Error Rate; Precision / Recall / F1 score; Exact Match (EM); Mean Reciprocal Rank (MRR)

Learn Before

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