learn-co-students / dsc-deep-learning-section-recap-seattle-ds-062419

Deep Neural Networks - Recap

Key Takeaways

The key takeaways from this section include:

• Deep neural network representations can lighten the burden and automate certain tasks of heavy data preprocessing
• Deep representations need exponentially fewer hidden units than shallow networks, to obtain the same performance
• Parameter initialization, forward propagation, cost function evaluation, and backward propagation are again the cornerstones of deep networks
• Tensors are the building blocks of neural networks and a good understanding of them and how to use them in Python is crucial
• Scalars can be seen as 0-D tensors. Vectors can be seen as 1-D tensors, and matrices as 2-D tensors
• The usage of tensors reaches beyond matrices: tensors can have N dimensions
• Tensors can be created and manipulated using Numpy
• Keras makes building neural networks in Python easy, and you learned how to do that in this section
• You can use Keras to do some NLP as well, e.g. for tokenization