Introduction to DL

DL is a subset of ML, a field of AI that focuses on teaching machines to learn from data and make decisions. DL particularly excels at learning from large volumes of data and has been the driving force behind many recent advancements in AI. Here are the key aspects of DL:

• Inspired by the human brain: DL models, particularly neural networks, are inspired by the structure and function of the human brain. They mimic the way biological neurons process information, although in a much-simplified form.
• Neural networks: The fundamental building blocks of DL are ANNs. These networks consist of layers of interconnected nodes or “neurons,” each of which performs simple computations. The complexity of DL comes from the depth of these layers, hence the term “deep” in DL.
• Learning from data: In DL, models learn directly from raw data. This ability is a significant departure from traditional ML algorithms, which often require manual feature...