In　order　to　generate　a　probability　density　function　(PDF)　for　fitting　the　probability　distributions　of　practical　data,　this　study　proposes　a　deep　learning　method　which　consists　of　two　stages:　(1)　a　training　stage　for　estimating　the　cumulative　distribution　function　(CDF)　and　(2)　a　performing　stage　for　predicting　the　corresponding　PDF.　The　CDFs　of　common　probability　distributions　can　be　utilised　as　activation　functions　in　the　hidden　layers　of　the　proposed　deep　learning　model　for　learning　actual　cumulative　probabilities,　and　the　differential　equation　of　the　trained　deep　learning　model　can　be　used　to　estimate　the　PDF.　Numerical　experiments　with　single　and　mixed　distributions　are　conducted　to　evaluate　the　performance　of　the　proposed　method.　The　experimental　results　show　that　the　values　of　both　CDF　and　PDF　can　be　precisely　estimated　by　the　proposed　method.　(C)　2019　Elsevier　B.V.　All　rights　reserved.