title: TensorFlow RandomForest vs Deep learning
body: <p>I am using TensorFlow for training model which has 1 output for the 4 inputs. The problem is of regression.</p>
  
  <p>I found that when I use RandomForest to train the model, it quickly converges and also runs well on the test data. But when I use a simple Neural network for the same problem, the loss(Random square error) does not converge. It gets stuck on a particular value. </p>
  
  <p>I tried increasing/decreasing number of hidden layers, increasing/decreasing learning rate. I also tried multiple optimizers and tried to train the model on both normalized and non-normalized data. </p>
  
  <p>I am new to this field but the literature that I have read so far vehemently asserts that the neural network should marginally and categorically work better than the random forest.</p>
  
  <p>What could be the reason behind non-convergence of the model in this case? </p>