Building Networks with Deep Learning
In the previous chapter, we explored machine learning (ML) concepts, including common strengths, weaknesses, pitfalls, and various popular ML algorithms.
In this chapter, we will explore artificial intelligence (AI) as we dive into deep learning (DL) concepts. We will review important neural network (NN) fundamentals, components, tasks, and DL architectures that are most common in data science interviews. In doing so, we will unravel the mysteries of weights, biases, activation functions, and loss functions while mastering the art of gradient descent and backpropagation.
Along the way, we’ll fine-tune our networks, delve into the magic of embeddings and autoencoders (AEs), and harness the transformative power of transformers. Plus, we’ll unlock the secrets of transfer learning (TL), understand why NNs are often referred to as “black boxes,” and explore common network architectures that have revolutionized industries...
