Considering　deep　learning＇s　importance　in　academic　research　and　industry　application,　this　paper　reviews　methods　and　applications　of　deep　learning.　First,　the　concept　of　deep　learning　is　introduced,　and　the　main　stream　deep　learning　algorithms　are　classified　into　three　classes:　feed-forward　deep　networks,　feed-back　deep　networks　and　bi-directional　deep　networks　according　to　the　architectural　characteristics.　Second,　network　architectures　and　training　methods　of　the　three　types　of　deep　networks　are　reviewed.　Finally,　state-of-the-art　applications　of　mainstream　deep　learning　algorithms　is　illustrated　and　trends　of　deep　learning　is　concluded.　Although　deep　learning　algorithms　outperform　traditional　methods　in　many　fields,　there　are　still　many　issues,　such　as　feature　learning　on　unlabeled　data;　the　balance　among　network　scale,　training　speed　and　accuracy;　and　model　fusion.　©,　2015,　Beijing　University　of　Technology.　All　right　reserved.