fueled by enterprises seeking greater insight from their analytics deep learning is now seeing widespread adoption while this artificial intelligence discipline was first conceived in the late nineteen fifties the recent jump into deep learning and other artificial intelligence methods is fueled by the recent increase in hardware power the explosion of big data and desire for greater insight in several key industries deep learning and artificial intelligence in general have taken off because organizations of all sizes and industries are capturing a greater variety of data and can mine bigger data including unstructured data such as text speech and images and it is not just large companies like amazon facebook and google that have big data it is everywhere deep learning needs big data and now we have it contrary to popular belief more data does not always mean better results however deep learning models absolutely thrive on big data through progressive learning they grind away and find nonlinear relationships in the data without requiring users to do feature engineering deep learning models also can overfit the training data so it is good to have lots of data to validate how well the model generalizes so what is a deep learning model it is essentially a neural network with many layers and these models can be enormous in size often with more than fifty million parameters the algorithm is not new but because we now have bigger data with more computing power this enables next generation deep learning applications such as computer vision or speech to text there are some considerations for those adopting deep learning consider the following big issues big data is expensive to collect label and store big models are hard to optimize big computations are often expensive