Just answered the great questions of the 2022 Stack Overflow Developer Survey and earned the Census - Badge, here I see the number of filled surveys and as an enthusiastic beginner in data science I am curious about the optimal representative participation in case of 17,866,773 total users!? I did a quick research, but stuck at the following sentence: "Once the population exceeds 20,000, your sample size will not change very much anymore." Please help me to understand this (agree/disagree …

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. Here is the Data I used: https://drive.google.com/open?id=1GLm87-5E_6YhUIPZ_CtQLV9F9wcGaTj2 Here is my code: #load and evaluate a saved model from numpy import loadtxt from tensorflow.keras.models import load_model …

I want to know if Deep learning can be used for Resume Parsing and scoring of the resume. Currently what I am doing is extracting the text from pdf or image using OCR/tesseract and finding the features like Email, Mobile No, Skills, Tenure, No of Companies, Awards etc from the text. So I have close to 100 features which are important for scoring the resume. Can we do similar thing using Deep learning and will the accuracy be better ? …

I’m giving my first steps with AI and Machine Learning so I have the following issue. I’m trying to predict an outcome from COVID-19 number of day vs confirmed cases using scikit-learn library. I mean, my input is the number of days since the pandemic started in my country and my output is the number of confirmed cases in that corresponding date. However both using GradientBoosting and RandomForest I get the same output values for the test values…I post below …

I'm trying to build an AI to play tictactoe(cs50ai pset0). i have built 7 essential functions for this purpose. 1.player function that takes as an argument a board and returns whose turn is it. actions function that takes as an argument a board and returns the possible actions on the board as a set of tubles. result function which takes an action and a board as arguments, and returns the new board caused of that action. winner function which takes …

I am using owl ontology for semantic analysis in emotional sentiment analysis project , I am trying to navigate the ontology to check a concepts and its relation , my ontology has classes like this : <!-- http://purl.obolibrary.org/obo/MFOEM_000011 --> <owl:Class rdf:about="http://purl.obolibrary.org/obo/MFOEM_000011"> <rdfs:subClassOf rdf:resource="http://purl.obolibrary.org/obo/MFOEM_000001" /> <rdfs:subClassOf> <owl:Restriction> <owl:onProperty rdf:resource="http://purl.obolibrary.org/obo/BFO_0000117" /> <owl:someValuesFrom rdf:resource="http://purl.obolibrary.org/obo/MFOEM_000208" /> </owl:Restriction> </rdfs:subClassOf> <obo:IAO_0000115>An unpleasant emotion closely related to anger but lower in intensity and without the moral dimension of blame and seriousness that is implicated in anger. [Source: …

I am new to data science and machine learning. Let's say that I have a question, and some correct answers for that question (for example, 10 correct answers). Is there a way to get a new answer as input, and "calculate" whether it is right? If you can recommend some readings, that would be great.

I’m trying to train an artificial intelligence model on OVHCloud AI Notebooks with the Common Voice’s dataset from Mozilla. The problem is that this database has a 70 GB size. I have tried to download it on my computer, and then to export the database to the OVHCloud Object Storage so I can use it, but this is excessively long. Is it possible to download directly this file on the notebook ? (My file is a tar.gz file)

I am using SVM for sentiment analysis project , I want to use n-skip gram for vector representation because I need to keep the semantic meaning between words , so I need to make vectors that belongs to a specified class be close to each other , how can I determine the most suitable n-skip to use? (means 2-skip or 3-skip or 4-skip ....), I am using doc2Vec in python.

I have started with RL and have some doubts regarding it. Does an RL agent learn during exploitation, or does it only learn during exploration? Is it possible to train a model only using exploitation (i.e. where exploration is not allowed)?

There are two closely related techniques in genetic programming. One of them is grammar based genetic programming (GBGP), which uses context free grammar to generate a derivative tree which represents the program. The other is grammatical evolution which uses something called genomes and codons, which is then mapped to a phenotype, a realization. The part where I get confused is, the phenotype realization can also be represented as a derivative tree. The codon to rule mapping is done through a …

I am new to prolog and need help on how to write a prolog program. Here is what i am trying to do. I have downloaded the dataset from this link - https://archive.ics.uci.edu/ml/datasets/Blood+Transfusion+Service+Center My goal is Use Any of the decision tree algorithms to build a decision tree for the given data Create rules from the decision tree. Code the rules into a Prolog Knowledge base Get the data about the blood donor from the user and predict if the …

Hi I am developing a reinforcement learning agent for a continous state/discrete action space. I am trying to use boltmzann/softmax exploration as action selection strategy. My action space is of size 5000. My implementation of boltzmann exploration: def get_action(state,episode,temperature = 1): state_encod = np.reshape(state, [1, state_size]) q_values = model.predict(state_encod) prob_act = np.empty(len(q_values[0])) for i in range(len(prob_act)): prob_act[i] = np.exp(q_values[0][i]/temperature) #numpy matrix element-wise division for denominator (sum of numerators) prob_act = np.true_divide(prob_act,sum(prob_act)) action_q_value = np.random.choice(q_values[0],p=prob_act) action_keys = np.where(q_values[0] == action_q_value) action_key …

I want to learn machine learning but I am more comfortable in C than python (also my pc is little slow), I tried to search but there are no tutorial or documentation for C. So, where can I start? Can you recommend some book or online course. Thanks for your time. Also how realistic it would be to learn on existing tutorials does api(like of tensorflow) have any changes in python and c in variables.

I was wondering if there are websites with which to stay constantly updated on the latest trends or topics on disruptive applications in artificial intelligence. I usually read scientific publications subject to university research, but I was wondering where to read applications, innovative and disruptive use cases.

How to apply reinforce/policy-gradient algorithms for continuous action space. I have learnt that one of the advantages of policy gradients is , it is applicable for continuous action space. One way I can think of is discretizing the action space same as the way we do it for dqn. Should we follow the same method for policy -gradient algorithms also ? Or is there any other way this is done? Thanks

I have an attribute that is the description of an operation (i.e description of a building consent), I need to translate this to a mathematical operation. I need to find out the new number of dwelling that is going to build, and I have to ignore any other operation. I am not sure how to tackle this problem. I can do Regex, and do lots of searches but there should be a smarter way (is there???) by using machine learning/text …

I am doing a project which requires a soft actor-critic reinforcement learning agent to learn how to reach a goal in a 100x100 maze environment as the one below: The state space is discrete and only the agent's current position is passed as the state. For example, the state is (50, 4) in the image. The action-space is also discrete and just includes [left, right, up, down, up-left, up-right, down-left, down-right]. The reward function is just 100 for reaching the …

I have a training dataset of images common images, there are more than 5K images in this dataset. But I have less memory in Google colab- RAM-12GB. I need to train all the images but due to less memory, I can't. What are the possible ways to train all the images with less memory? I have an idea, but don't know it is an optimal solution, which is I split the dataset into 5 sets[each set contains 1000 images] and …

I have a dataset with the following x columns: date time is_weekend is_holiday start_intersection end_intersection The output is a list of intersections, that connect start_intersection with end_intersection. The list is essentially the shortest paths from start to finish Is there any graph ai models that can generate such shortest path?