In this chapter, we learned how neural networks actually work followed by building a neural network to classify handwritten digits using TensorFlow. We also saw different types of neural networks such as an RNN, which can remember information in the memory. Then, we saw the LSTM network, which is used to overcome the vanishing gradient problem by keeping several gates to retain information in the memory as long as it is required. We also saw another interesting neural network for recognizing images called CNN. We saw how CNN use different layers to understand the image. Following this, we learned how to build a CNN to recognize fashion products using TensorFlow.
In the next chapter, Chapter 8, Atari Games With Deep Q Network, we will see how neural networks will actually help our RL agents to learn more efficiently.
