The human genome contains the fundamental code that defines the identity and function of all the cell types and tissues in the human body. Genes are functional units of DNA sequence that encode for proteins. But genes account for just about 2% of the 3 billion long human genome sequence. What does the rest of the genome encode? How is gene activity controlled in each cell type? Where do the regulatory control elements lie and what is their sequence composition? How do variations and mutations in the genome sequence affect cellular function and disease? These are fundamental questions that remain largely unanswered. The regulatory code that controls gene activity is made up complex genome sequence grammars encoded in millions of hierarchically organized units of regulatory elements. These functional words and grammars are sparsely distributed across the genome and remain largely elusive. Deep learning has revolutionized our understanding of natural language, speech and vision. We strongly believe it has the potential to revolutionize our understanding of the regulatory language of the genome. We have developed integrative supervised deep learning frameworks to learn how genomic sequence encodes millions of experimentally measured regulatory genomic events across 100s of cell types and tissues. We have developed novel methods to interpret our models and extract local and global predictive patterns revealing many insights into the regulatory code. We demonstrate how our deep learning models can reveal the regulatory code that controls differentiation and identity of variety of blood cell types. Our models also allow us to predict the effects of natural and disease-associated genetic variation, that is, how changes in the DNA sequence are likely to affect molecular mechanisms associated with various complex diseases such as coronary heart disease.