320 points by deeplearner 1 year ago flag hide 15 comments
deeplearningfan 1 year ago next
This is such an informative article about the math behind deep learning! Kudos to the author!
mathwiz 1 year ago prev next
I appreciate the effort to break down the complex math concepts in deep learning. However, I'd suggest adding more on how activation functions work in neural networks.
deeplearningfan 1 year ago next
Thank you for the suggestion, MathWiz! I'll make sure to include more details on activation functions in my next article.
aihunter 1 year ago prev next
I've been following deep learning for a few years now and this is one of the best explanations of the underlying math I've come across. Great job!
neuralnetnovice 1 year ago prev next
I'm new to neural networks and find the math difficult to understand. Is there a resource you'd recommend for breaking down the concepts in a more beginner-friendly way?
mathwiz 1 year ago next
@NeuralNetNovice, I'd recommend starting with the basics of linear algebra and calculus to get a stronger foundation. Then, check out resources like 3Blue1Brown's 'Essence of Linear Algebra' and 'Essence of Calculus' series on YouTube. They're visually engaging and break down complex concepts in a digestible way.
datascientist 1 year ago prev next
I'm not sure if I agree with the author's statement that neural networks have 'outperformed' traditional machine learning methods. There are still cases where traditional methods are more efficient and accurate.
deeplearningfan 1 year ago next
Thank you for bringing up a good point, DataScientist. There are indeed cases where traditional machine learning methods are more suitable. I'll make sure to include this in my next article to provide a more balanced view.
machinelearningenthusiast 1 year ago prev next
This article is a great starting point, but I'd love to see a follow-up discussing the differences in performance and applications between fully connected and convolutional neural networks.
deeplearningfan 1 year ago next
Thank you for the suggestion, MachineLearningEnthusiast! I'll make sure to include a section on the differences between fully connected and convolutional neural networks in my next article.
codemonkey 1 year ago prev next
I'm having a tough time visualizing how backpropagation works when calculating gradients. Can anyone recommend a resource for visualizing this concept?
aihunter 1 year ago next
@CodeMonkey, I'd recommend checking out this interactive backpropagation visualization: http://tutorials.tegonal.com/chapter/deep%20learning/interactive%20backpropagation.html. It really helped me grasp the concept of how backpropagation works.
optimizationguru 1 year ago prev next
I appreciate the detailed explanation of gradient descent, but I think it's important to note that there are alternative optimization methods (e.g., Adam, RMSProp) that perform better for certain types of neural networks. Would you agree?
deeplearningfan 1 year ago next
Absolutely, OptimizationGuru! Alternative optimization methods like Adam and RMSProp are crucial to consider for different types of neural networks. I'll make sure to include a section discussing these methods in my next article.
tensorflowninja 1 year ago prev next
This is a great resource for anyone looking to understand the math behind deep learning! I only wish it had more focus on TensorFlow implementation details rather than just the theory.