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## Why do Bayesian algorithms work good with small datasets?

I read very often that Bayesian algoriths are working good on small datasets why is that? (I think it is because it is good at generalizing, but why is that?) https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=7636&context=etd https://math.stackexchange.com/questions/2589224/how-is-bayesian-inference-better-than-classical-inference-on-small-samples What is the reason why Bayesian works good at small datasets?

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## How does the neural-network know how to tweak weights for a specific neuron?

I know backpropagation uses cost and gradient descent to tweak the weights to minimize the cost. But how does it know which weights to give more weight to in the first place? Is there something inside each neuron in the hidden layers that defines how this is an important neuron for the correct result in […]

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## create a minimization function

I am not sure if I am in the right place but I am trying to find some infos or tutorial in one of my problem and I do not know where to look at or how this is call to help me in my research… I am a variable, let’s say it’s a grade […]

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## Neural network is not giving the expected output after training in Python

My neural network is not giving the expected output after training in Python. Is there any error in the code? Is there any way to reduce the mean squared error (MSE)? I tried to train (Run the program) the network repeatedly but it is not learning, instead it is giving the same MSE and output. […]

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## Normalizing Normal Distributions in Thompson Sampling for online Reinforcement Learning

In my implementation of Thompson sampling (TS) for online Reinforcement Learning, my distribution for selecting $a$ is $\mathcal{N}(Q(s, a), \frac{1}{C(a)+1})$ where $C(a)$ is the number of times $a$ has been picked. However, I found that this does not work well in some cases depending on the magnitude of $Q(s,a)$. For example, if $Q(s_i,a_1) = 100$, […]

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## learning object recognition of primitive shapes through transfer learning problem

Question on transfer learning object classification (MobileNet_v2 with 75% number of parameters) with my own synthetic data: I made my own dataset of three shapes: triangles, rectangles and spheres. each category has 460 samples with diferent sizes, dimensions, different wobbles at edges. They look like this: I want the network to classify these primitive shapes […]

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## Are there any public real-life code examples of ML applications in Python?

Problems I often face at work usually differ from tutorial or book-like examples so I end up with a code that works but it’s not elegant and takes too much time to write. I wanted to ask you if there are some publicly accesible examples or repositories of Python codes that deal with machine learning […]

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## The Evolutionary Game Theory approach and Meta-Heuristics approach are same?

Here the list of Meta Heuristic Algorithm list : Ant colony optimization, Ant lion optimizer, Artificial bee colony algorithm, Bat algorithm, Cat swarm optimization, Crow search algorithm, Cuckoo optimization algorithm, Cuckoo search algorithm, Differential evolution, Firefly algorithm, Genetic algorithm, Glowworm swarm optimization, Gravitational search algorithm, Grey wolf optimizer, Harmony search, Multi-verse optimizer, Particle swarm optimization, […]

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## How can I read input node and output node from frozen tensorflow model?

I want to read and print input node and output node from trained frozen tensorflow model, e.g., frozen_inference_model.pb How can I read it in python tensorflow? Thanks

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## Object Detection and Number of Products

Suppose we have $1000$ products that we want to detect. For each of these products, we have $500$ training images/annotations. Thus we have $500,000$ training images/associated annotations. If we want to train a good object detection algorithm to recognize these objects (e.g. YOLO) would it be better to have multiple detection models? In other words, […]