In this work we applied a feed forward neural network to solve Blasius equation which is a thirdorder nonlinear differential equation. Blasius equation is a kind of boundary layer flow. We solved Blasius equation without reducing it into a system of first order equation. Numerical results are presented and a comparison according to some studies is made in the form of their results. Obtained results are found to be in good agreement with the given studies. I. * Electronic address: halilmutuk@gmail.com arXiv:1811.08936v1 [cs.LG] 8 Nov 2018 * The neural network based solution of a differential equation is differentiable and is in closed analytic form that can be used in any subsequent calculation. On the other hand most other techniques offer a discrete solution or a solution of limited differentiability. * Trial solutions of ANN include a single independent variable regardless of the dimension of the problem. * The neural network based method to solve a differential equation provides a solution with very good generalization properties. * The solutions are continuous over all the domain of the integration. Traditional numerical methods provide solutions over discrete points and the solution among these points must be interpolated. * The required number of model parameters is far less than any other solution technique and therefore, compact solution models are obtained, with very low demand on memory space. * The method is general and can be applied to ordinary differential equations (ODEs), systems of ODEs and to partial differential equations (PDEs) as well. * The method is general and can be applied to the systems defined on either orthogonal box boundaries or on irregular arbitrary shaped boundaries. * The method can be realized in hardware, using neuroprocessors, and hence offer the opportunity to tackle in real time difficult differential equation problems arising in many engineering applications. * The method can also be implemented on parallel architectures.