First in the lecture series on deep learning for astronomy. This series is designed to provide clear and accessible introductions to key deep learning techniques, with examples of their applications to astronomical data and research. In the first session we shall provide a brief history of deep learning and introduce the intuition and basic math behind NNs, together with examples related to data fitting with basic NN (slides and code provided in .https://github.com/cwestend/IACDEEP_introNN/)