Deep Learning with Python-A Hands-on Introduction下载

Python 深度学习实战教程 The field of Artificial Intelligence (AI), which can definitely be considered to be the parent field of deep learning, has a rich history going back to 1950. While we will not cover this history in much detail, we will go over some of the key turning points in the field, which will lead us to deep learning. Tasks that AI focused on in its early days were tasks that could be easily described formally, like the game of checkers or chess. This notion of being able to easily describe the task formally is at the heart of what can or cannot be done easily by a computer program. For instance, consider the game of chess. The formal description of the game of chess would be the representation of the board, a description of how each of the pieces move, the starting configuration, and a description of the configuration wherein the game terminates. With these notions formalized, it's relatively easy to model a chess-playing AI program as a search and, given sufficient computational resources, it’s possible to produces a relatively good chess-playing AI. The first era of AI focused on such tasks with a fair amount of success. At the heart of the methodology was a symbolic representation of the domain and the manipulation of symbols based on given rules (with increasingly sophisticated algorithms for searching the solution space to arrive at a solution). It must be noted that the formal definitions of such rules were done manually. However, such early AI systems were fairly general purpose task/problem solvers in the sense that any problem that could be described formally could be solved with the generic approach.