Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, NLP, and Transformers using TensorFlow

Chapter 5

Toward DL: Frameworks and Network Tweaks

An obvious next step would be to see if adding more layers to our neural networks results in even better accuracy. However, it turns out getting deeper networks to learn well is a major obstacle. A number of innovations were needed to overcome these obstacles and enable deep learning (DL). We introduce the most important ones later in this chapter, but before doing so, we explain how to use a DL framework. The benefit of using a DL framework is that we do not need to implement all these new techniques from scratch in our neural network. The downside is that you will not deal with the details in as much depth as in previous chapters. You now have a solid enough foundation to build on. Now we ...