breast cancer dataset one possible inference we can make is that features that have very small weights for all of the hidden units are "less importantto the model we can see that "mean smoothnessand "mean compactness,in addition to the features found between "smoothness errorand "fractal dimension error,have relatively low weights compared to other features this could mean that these are less important features or possibly that we didn' represent them in way that the neural network could use we could also visualize the weights connecting the hidden layer to the output layerbut those are even harder to interpret while the mlpclassifier and mlpregressor provide easy-to-use interfaces for the most common neural network architecturesthey only capture small subset of what is possible with neural networks if you are interested in working with more flexible or larger modelswe encourage you to look beyond scikit-learn into the fantastic deep learning libraries that are out there for python usersthe most well-established are keraslasagnaand tensor-flow lasagna builds on the theano librarywhile keras can use either tensor-flow or theano these libraries provide much more flexible interface to build neural networks and track the rapid progress in deep learning research all of the popular deep learning libraries also allow the use of highperformance graphics processing units (gpus)which scikit-learn does not support using gpus allows us to accelerate computations by factors of to xand they are essential for applying deep learning methods to large-scale datasets strengthsweaknessesand parameters neural networks have reemerged as state-of-the-art models in many applications of machine learning one of their main advantages is that they are able to capture information contained in large amounts of data and build incredibly complex models given enough computation timedataand careful tuning of the parametersneural networks often beat other machine learning algorithms (for classification and regression taskssupervised machine learning algorithms