Summary
In this chapter, we worked on the stock prediction project again, but with neural networks this time. We started with a detailed explanation of neural networks, including the essential components (layers, activations, feedforward, and backpropagation), and transitioned to DL. We moved on to implementations from scratch with scikit-learn, TensorFlow, and PyTorch. We also learned about ways to avoid overfitting, such as dropout and early stopping. Finally, we applied what we covered in this chapter to solve our stock price prediction problem.
In the next chapter, we will explore NLP techniques and unsupervised learning.
