# Tag Info

### Is there a better way to do this type of optimization?

Even you trained your classifier using CE loss, in practice the usual overfitting, imbalanced training data and distribution shift may result in poorly calibrated predictions as discussed in a recent ...
• 2,821

### Deep Learning training strategy: Avoid shuffling individual training images, instead shuffle batches?

In Pytorch - DataLoader - If you set Shuffle to false , it will select the samples as they are stored - Sequential Access Problems with Shuffle set to false : Gneralization, Overfitting. If the model ...
• 11
1 vote

### What Are the Use Cases for Machine Learning or even NLP for Bitcoin Market Making?

Since handling post-trade compliance in financial applications involves monitoring and analyzing large volumes of transaction data to ensure adherence to regulatory requirements, this task often ...
• 2,821
1 vote

### What is the best machine learning textbook for decision trees?

Favorite of mine: "Tree-based Machine Learning Algorithms: Decision Trees, Random Forests, and Boosting" by Ding-Zhu Du and Ker-I Ko
• 11

### How do you interpret this train vs test accuracy scores? is the model under or over fitting?

From the roc_auc metric it does not show the model is in the overfit zone. It tells that on average of 10 predictions 8 predictions could be right and 2 predictions could go wrong. Now , it depends ...
• 11

### Theoretical justification for data augmentation

Data augmentation helps resolve overfitting model by introducing more variability to the training data. If your model does not have overfitting problem, data augmentation may not be needed.
• 11

### Theoretical justification for data augmentation

Your intuition about the need for data augmentation transformations to be consistent with the data generating distribution is correct. For your digits problem example, if your test data has no or very ...
• 2,821

### Theoretical justification for data augmentation

So, first of all, let me specify that we deal with a finite set of $x,y\sim p(x,y)$, and not $p(x,y)$ directly, which is definitely a whole another different story Why, well, because we then have an ...
• 2,378

### Should I use reinforcement learning to automate the throw of a ball in a Pokemon game?

Yes this is indeed a very plausible approach ! Mind however, that RL is not very sample efficient, so you need a lot of runs for try and error. So it essentially boils down to the question whether you ...

### What is convergence in machine learning?

In Machine(or Deep) Learning, Convergence means an initial weight moves towards the global minimum of a function by gradient descent as shown below:

### Why is Batch Gradient Descent performing worse than Stochastic and Mini-Batch Gradient Descent?

Because the computation of Batch Gradient Descent(BGD) is more stable(less fluctuated) than Mini-Batch Gradient Descent(MBGD) and Stochastic Gradient Descent(SGD), so BGD less easily(more difficultly) ...
Accepted

### Why is gradient clipping not preventing my gradient descent from going out of bounds?

Gradient clipping just clips the magnitude of the gradient, it has no effect on the values themselves (if you have x=3 and your gradient as g=4, then if you do x - g, you will get -1. You can clip the ...
• 56

### Why exactly do we need the learning rate in gradient descent?

Your intuition is correct and the short answer is: The learning rate is necessary in order to control the size of the update! You probably already know that gradient descent (GD) is derived from the [...
• 56

### Why exactly do we need the learning rate in gradient descent?

Indeed your intuition of Learning rate is right regarding its additional control in training the model, and the precise terminology for your somewhat vague 'additional control' intuition even in a ...
• 2,821

### Why exactly do we need the learning rate in gradient descent?

In short, there are two major reasons: The optimization landscape in parameter space is non-convex even with convex loss function (e.g., MSE). Therefore, you need to do small update steps (i.e., the ...
• 3,046

### How does Alibaba Cloud's Machine Learning Platform for AI (PAI) support model training and deployment?

How PAI supports each phase of the machine learning lifecycle: 1. Model Training Features and Capabilities: Pre-built Algorithms: PAI provides a rich library of pre-built algorithms, covering a wide ...

### Why the cost/loss starts to increase for some iterations during the training phase?

After reading your comments, I assume you are doing full-batch training instead of mini-batch training. Therefore, stochasticity is not a factor in this part. Despite the training batch size, I couldn’...
1 vote

### Can software testers transfer their skills into adversarial testing for AI/LLMS?

One easy way to test language models is to provide prompts and check if they return an acceptable answer (which you should define) or they don't return a bad answer, for example, a factually wrong ...
• 41k
1 vote
Accepted

### Does Machine Learning focus on discriminative AI while Deep Learning also focus on generative AI?

It's not accurate to say that classical machine learning implements only discriminative AI while deep learning implements both generative and discriminative AI. Both classical machine learning and ...
• 2,821

Top 50 recent answers are included