Intro to Deep Learning

eep learning networks are mechanical systems which “learn” by modeling high-level abstractions in datasets, and cycling through trial-and-error guesses with feedback, to establish an optimally-weighted system that can make accurate predictions about new data. LSTM RNNs (long short term memory Recurrent Neural Nets) is a deep learning method that overcomes some of the limitations in classical time series forecasting (non-linearity, fixed-time windows, time lag specification, and multivariate forecasting). Hence, deep learning is emerging as an important forecasting technique for coping with exponentially growing data, and the ability to expediently process and direct these data. In the future, deep learning smart networks might provide an advanced computational infrastructure for tackling real-time predictive data science problems such global health monitoring, energy storage and transmission, and financial risk assessment. Slides: https://www.slideshare.net/lab....logga/deep-learning-