For questions surrounding gradient descent, a method for finding the optimum state of a parameterized function based on another function often called the loss or error function. It iteratively descends the loss surface to the minimum loss by adjusting parameters based on the product of the partial derivatives comprising the gradient and a learning rate.

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### Is neural networks training done one-by-one? [duplicate]

I'm trying to learn neural networks by watching this series of videos and implementing a simple neural network in Python. Here's one of the things I'm wondering about: I'm training the neural network ...
3k views

### Is the mean-squared error always convex in the context of neural networks?

Multiple resources I referred to mention that MSE is great because it's convex. But I don't get how, especially in the context of neural networks. Let's say we have the following: $X$: training ...
2k views

### Can non-differentiable layer be used in a neural network, if it's not learned?

For example, AFAIK pooling layer in CNN is not differentiable, but it can be used because it's not learning. Is it always true?
891 views

### What are the best known gradient-free training methods for deep learning?

As I know, the current state of the art methods for training deep learning networks are variants of gradient descent or stochastic gradient descent. What are the best known gradient-free training ...
174 views

### Why is the perceptron criterion function differentiable?

I'm reading chapter one of the book called Neural Networks and Deep Learning from Aggarwal. In section 1.2.1.1 of the book, I'm learning about the perceptron. One thing that book says is, if we use ...
325 views

### Is there an ideal range of learning rate which always gives a good result almost in all problems?

I once read somewhere that there is a range of learning rate within which learning is optimal in almost all the cases, but I can't find any literature about it. All I could get is the following graph ...
749 views

### What is the formula for the momentum and Adam optimisers?

In the gradient descent algorithm, the formula to update the weight $w$, which has $g$ as the partial gradient of the loss function with respect to it, is: $$w\ -= r \times g$$ where $r$ is the ...
2k views

### What is the relationship between gradient accumulation and batch size?

I am currently training some models using gradient accumulation since the model batches do not fit in GPU memory. Since I am using gradient accumulation, I had to tweak the training configuration a ...
2k views

### CNN backpropagation with stride>1

I read that to compute the derivative of the error with respect to the input of a convolution layer is the same to make of a convolution between deltas of the next layer and the weight matrix rotated ...
7k views

### How is the gradient calculated for the middle layer's weights?

I am trying to understand backpropagation. I used a simple neural network with one input $x$, one hidden layer $h$ and one output layer $y$, with weight $w_1$ connecting $x$ to $h$, and $w_2$ ...
3k views

### How to avoid falling into the “local minima” trap?

How do I avoid my gradient descent algorithm into falling into the "local minima" trap while backpropogating on my neural network? Are there any methods which help me avoid it?
984 views

### How is local minima possible in gradient descent?

Gradient descent works on the equation of mean squared error, which is an equation of a parabola $y=x^2$. We often say that weight adjustment in a neural network by gradient descent algorithm can hit ...
505 views

### Can neuroevolution be combined with gradient descent?

Is there any precedent for using a neuroevolution algorithm, like NEAT, as a way of getting to an initialization of weights for a network that can then be fine-tuned with gradient descent and back-...
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### Why are most commonly used activation functions continuous?

I have come to notice that the most commonly used activation functions are continuous. Is there any specific reason behind this? Results such as this paper have worked on training networks with ...
461 views

### What is the advantage of using cross entropy loss & softmax?

I am trying to do the standard MNIST dataset image recognition test with a standard feed forward NN, but my network failed pretty badly. Now I have debugged it quite a lot and found & fixed some ...
735 views

### What's the function that SGD takes to calculate the gradient?

I'm struggling to fully understand the stochastic gradient descent algorithm. I know that gradient descent allows you to find the local minimum of a function. What I don't know is what exactly that ...
73 views

### Why is the learning rate generally beneath 1?

In all examples I've ever seen, the learning rate of an optimisation method is always less than $1$. However, I've never found an explanation as to why this is. In addition to that, there are some ...
121 views

### Are filters fixed or learned?

No matter what I google or what paper I read, I can't find an answer to my question. In a deep convolutional neural network, let's say AlexNet (Krizhevsky, 2012), filters' weights are learned by means ...
350 views

### Does Musk know what gradient descent is?

From Futurism.com: Musk indicates that internet infrastructure is "particularly susceptible" to a method called gradient descent algorithm, a mathematical problem-solving process. Bad news is, AI ...
142 views

### How to obtain a formula for loss, when given an iterative update rule in gradient descent?

From the reinforcement learning book section 13.3: Using pytorch, I need to calculate a loss, and then the gradient is calculated internally. How to obtain the loss from equations which are stated ...
3k views

### How do I calculate the gradient of the hinge loss function?

With reference to the research paper entitled Sentiment Embeddings with Applications to Sentiment Analysis, I am trying to implement its sentiment ranking model in Python, for which I am required to ...
542 views

### Why Feature Scaling for skewed contour?

Why is it that the skewed contour (unscaled features) will result in slow performance of gradient descent? In other words, how (or why) will the gradients end up taking a long time before finding the ...
829 views

Semi-gradient methods work well in reinforcement learning, but what is there a reason of not using the true gradient if it can be computed? I tried it on the cart pole problem with a deep Q-network ...
Let us assume we have a GRU network containing $H$ layers to process a training dataset with $K$ tuples, $I$ features, and $H_i$ nodes in each layer. I have a pretty basic idea how the complexity of ...