Contents
How can I improve my neural network results?
Now we’ll check out the proven way to improve the performance(Speed and Accuracy both) of neural network models:
- Increase hidden Layers.
- Change Activation function.
- Change Activation function in Output layer.
- Increase number of neurons.
- Weight initialization.
- More data.
- Normalizing/Scaling data.
Which of the following can be used to increase training speed of neural network?
For example, GPUs and TPUs optimize for highly parallelizable matrix operations, which are core components of neural network training algorithms. These accelerators, at a high level, can speed up training in two ways.
How is Relu improving the performance of neural networks?
But, they suffered from the problem of vanishing gradients, i.e during backpropagation, the gradients diminish in value when they reach the beginning layers. This stopped the neural network from scaling to bigger sizes with more layers. ReLU was able to overcome this problem and hence allowed neural networks to be of large sizes.
Why do you have to train a neural network?
Therefore, you have to train the network for a longer period of time. A small learning rate also makes the network susceptible to getting stuck in local minimum. i.e the network will converge onto a local minima and unable to come out of it due to the small learning rate. Therefore, you must be careful while setting the learning rate.
How are neural networks used in data science?
Neural networks are machine learning algorithms that provide state of the accuracy on many use cases. But, a lot of times the accuracy of the network we are building might not be satisfactory or might not take us to the top positions on the leaderboard in data science competitions.
How to identify if your neural network is overfitting?
How to identify if your model is overfitting? you can just cross check the training accuracy and testing accuracy. If training accuracy is much higher than testing accuracy then you can posit that your model has overfitted. You can also plot the predicted points on a graph to verify. There are some techniques to avoid overfitting: