@babbarsome Thanks! I think you need to include the line “app.config.suppress_callback_exceptions = True”. You can read about it here: https://t.co/XoRDLWhqDV
@kierisi I recently listened to the @tferriss podcast with @SafiBahcall. And he has an interesting concept called FBR (at 1:06:42 in the video): https://t.co/zlOfOVToK3
@ValechaJagrati Normally, it shouldn’t improve the accuracy if you consider all the other potential splits. After all, we are choosing THE best split. The sklearn library probably uses many more potential parameters than just the max depth or min samples. So, you might look into them.
@jakevdp I just started to play around with plotly and dash to create my own personal dashboard. And there I also have to tinker a little bit with CSS. So, it is somewhat relieving to hear that even non-beginners/experts don’t think it is easy or straight-forward.
@hugobowne I really like your podcast. It’s so interesting to learn about the experiences of people working in data science. It’s also really great that your interview partners come from so many different industries and backgrounds.
Can I ask you how you record the interviews?
@iamtrask … But, I think, that is not really the problem (think ReLUs). The bigger problem is that the derivative of the step function is zero everywhere else. So, because of the chain rule in backprop, the weight updates would always be zero.
@iamtrask … in introductions to deep learning, the step function is often used first to explain the purpose of activation functions. Then, it is replaced with the sigmoid function since the step function is not differentiable at the step…