Tensorflow predict işler

The job is installing and testing a python app on a Windows 64-bit compatible device. This requires a USB camera and computer that is dedicated for testing. The app will prompt...app. Major requirements: - Device should be comptible with Windows Pro 11 64-bit - Device must work with an external USB camera. - Fast Internet connection to download the app and its python dependencies - Knowledge in python and powershell because you will need this when installing the app. - You should be willing to share the captured videos with me. The app makes use of machine learning to predict objects and scenes, so the video recordings should be shared with me. - If you are a manager or group leader, that would be a plus. I would like to know your feedback about using the app in your organization ...

...Docker Image Version (latest by date) GitHub Release Date GitHub Workflow Status Downloads Downloads codecov Code style: black Join the chat at darts is a Python library for easy manipulation and forecasting of time series. It contains a variety of models, from classics such as ARIMA to deep neural networks. The models can all be used in the same way, using fit() and predict() functions, similar to scikit-learn. The library also makes it easy to backtest models, combine the predictions of several models, and take external data into account. Darts supports both univariate and multivariate time series and models. The ML-based models can be trained on potentially large datasets containing multiple time series, and some of the models offer a rich support for probabilistic

The file Download temperature records (in tenths of degrees C) for a weather station in nearby Brookville, Indiana from May 1925 through most of 2022. This data was obtained from the National Centers for Environmental Information (NCEI) within the National Oceanic and Atmosph...are 32600 unique dates in that window after excluding February 29th. Clean up your Table in Part 1 with kableExtra: to an external site. For the Local Linear Regression Smoother values Use the built in function loess() -- it works just like lm() -- span=0.5 or 0.75 works well for this plot You can use predict() on the fitted loess object to get the values for the plot

I have 6 months data of wall temperature, i need to predict the next six months data

Create a function which makes a 2D graph with nodes and edges. Then build a GNN to approximate this graph! Must use tensorflow

A csv file to analysis the default rate of hotel cancellation to predict the potential ID to cancel the booking .

I need to predict the World Cup winner/finalist using R. I need this by 10pm today. Predict World Cup winner by using simulations, anova, logistic regression, visualizations, random forest, etc

Hello I am looking for Tensorflow model that has as a input the voltage value of a battery and detects a specific waveform that a specific load applied to it . How much would that cost?

I have a data set which consists of 54 labels. need to predict the exact label for the input. input is also text.

Predict Diabetes in a patient with various classification techniques. All the relevant information and data is given in the following files.

Use Data mining to predict Diabetes in a patient using different classification techniques. Please go through the files below so you have a clear idea of what is required out of this.

What do I want? (requirements) - A good ISI journal paper titled: "Proposed Machine Learning Enabled Framework to Predict Consumer Behavior in Retail Sector". - minimum sections: introduction, background, contribution, proposed framework, discussion, conclusion, reference. - use good reference ISI journals papers and Q1/Q3 journal papers. The main point is to cover all sections and make it an ISI journal-level paper and use the attached file as a guideline for the paper. Journal paper usually between 7-10 pages if we are talking about IEEE format and the reference format .etc.

What do I want? (requirements) - A good ISI journal paper titled: "Proposed Machine Learning Enabled Framework to Predict Consumer Behavior in Retail Sector". - minimum sections: introduction, background, contribution, proposed framework, discussion, conclusion, reference. - use good reference ISI journals papers and Q1/Q3 journal papers. The main point is to cover all sections and make it an ISI journal-level paper and use the attached file as a guideline for the paper. Journal paper usually between 7-10 pages if we are talking about IEEE format and the reference format .etc.

In the normal snakes and ladders, the game is pure luck (the agent does not choose an action, just follows the dice). So I will require a modification to the rules where the player can choose how many states to advance, limited by the dice roll. In other words, if they roll a 3, then they can choose to advance 0, 1, 2, or 3 states. Instead I’d recommend depth-li...require a modification to the rules where the player can choose how many states to advance, limited by the dice roll. In other words, if they roll a 3, then they can choose to advance 0, 1, 2, or 3 states. Instead I’d recommend depth-limited expectimax, using an evaluation function (similar to a heuristic) to estimate the risk/reward of states at the depth limit. Lastly, you can use the NN to predict risk/r...

I have a object detection model of tensorflow working in dotNet and python script of text detection with modal. I would like it to work in python. object detection one. Version of *Python, *Opencv, *tensorflow. Input is a capture image from camera(probably with overlay), Output: cropped license plate, rectangle on original input image, license plate text It should be able to work on linux

...----------- tensorboard>=2.4.1 # wallb # clearml # Plot ------------------------------------ pandas>=1.1.4 seaborn>=0.11.0 # Export ------------------------------------- # coremltools>=6.0 # CoreML export # onnx>=1.9.0 # ONNX export # onnx-simplifier>=0.4.1 # ONNX simplifier # nvidia-pyindex # TensorRT export # nvidia-tensorrt # TensorRT export # scikit-learn<=1.1.2 # CoreML quantization # tensorflow>=2.4.1 # TF exports (-cpu, -aarch64, -macos) # tensorflowjs>=3.9.0 # export # openvino-dev # OpenVINO export # Deploy -------------------------------------- # tritonclient[all]~=2.24.0 # Tools -------------------------------------- ipython # interactive notebook psutil # system utilization thop>=0.1.1 # FLOP's computation # mss # screenshots # ...

What do I want? (requirements) - A good ISI journal paper titled: "Proposed Machine Learning Enabled Framework to Predict Consumer Behavior in Retail Sector". - minimum sections: introduction, background, contribution, proposed framework, discussion, conclusion, reference. - use good reference ISI journals papers and Q1/Q3 journal papers.

What I want? (requirements) - A good ISI journal paper titled: "Proposed Machine Learning Enabled Framework to Predict Consumer Behavior in Retail Sector". - minimum sections: introduction, background, contribution, proposed framework, discussion, conclusion, reference. - use good reference ISI journals paper and Q1/Q3 journal papers.

We would like to be able to model gas flare combustion, with the goal of being able to model / predict the quantities of pollution including unburnt methane and CO2 released into the environment, in realtime during operations. Modelling various changing parameters, including; - Varying gas composition - Varying added air % to mix - Varying exit velocity / pipe ID - Varying environmental, wind temperature effect

About this dataset The contains data on popular Spotify songs and artists, as well as information on artist followers and song genres. This data can be used to predict artist s success and song popularity How to use the dataset This dataset can be used to predict an artist's success on Spotify. The data includes features such as the artist's name, genre, popularity, number of followers, and release history. The data can be used to understand the popularity of different genres of music, as well as to predict an artist's success on Spotify Research Ideas Predict the popularity of future Spotify releases. Identify new and upcoming artists likely to achieve commercial success. Understand the popularity of different genres of music on Spotify. This is...

About this dataset The contains data on popular Spotify songs and artists, as well as information on artist followers and song genres. This data can be used to predict artist s success and song popularity How to use the dataset This dataset can be used to predict an artist's success on Spotify. The data includes features such as the artist's name, genre, popularity, number of followers, and release history. The data can be used to understand the popularity of different genres of music, as well as to predict an artist's success on Spotify Research Ideas Predict the popularity of future Spotify releases. Identify new and upcoming artists likely to achieve commercial success. Understand the popularity of different genres of music on Spotify. This is...

I need someone advanced in programming to write me code for ML in trading view to predict future volume, RSI, and or/ MACD

...this code works. Can you explain what is the relation between x_input, y_input, input-var I made and the predicted output. Why the output is just one value and why does this program also work with y_input 5 values versus 1. So basically, how does this program work and how can I use a program like this for predicting a sequence of numbers? import keras import numpy as np import logging import tensorflow from import Dense from import Sequential # if __name__ == '__main__': print('PyCharm') x_input = ([[1, 2, 3, 4, 5]]) y_input = ([[4]]) model = Sequential() (Dense(units=32, activation="tanh", input_dim=[1], kernel_initializer='random_normal')) (Dense(units=1, kernel_initializer='random_normal')) model

We have a model to predict when a patient needs to be admitted to the ICU, but we need someone with strong programming experience to do the replay of historical data through the model.

Hello everyone! We want to predict and make Recommendation using Python.

I'm using the free prediction building and would like a statistical modelling expert who has experience using Einstein Predictions to help me predict when something will happen. I have millions of records and each month 100k + of outcomes records so I'm expecting a highly accurate model. Please do not message me if you haven't read this brief and if you don't have the relevant experience.

...explain how they did it. Using a dataset ( the "Adult Data Set") from the UCI Machine-Learning Repository we can predict based on a number of factors whether or not someone's income will be greater than $50,000. The technique The approach is to create a 'classifier' - a program that takes a new example record and, based on previous examples, determines which 'class' it belongs to. In this problem we consider attributes of records and separate these into two broad classes, <=50K and >50K. We begin with a training data set - examples with known solutions. The classifier looks for patterns that indicate classification. These patterns can be applied against new data to predict outcomes. If we already know the outcomes of the test d...

Train TFLite Model with existing Tensorflow TFRecord and COCO Datasets. All ready have 6000 annotated images.

Hi Experts, I have trained a few models, I need you to do the following: 1.) Make live predictions given a new test set. N/B: The new test set does not have the features in the training set. The features were created using preprocessing and feature engineering. 2.) Preprocess the new set to same shape as Xtest (using the existing codes) so you can do (1) above. Budget: $15 Timeline 1hr Codes attached

i have datasets in google drive need to develop script in colab to train and export tf model. Need to work remotely **Note - Urgent Budget - 1500 - 2000

I need a TF Devloper to train a model and export it . data format in PASCAL need to use Colab. Budjet 1000 inr to 2000

Use RStudio to find variables correlation and then predict the alcohol consumption and predict grades. This is the link to the dataset: ＝251&searchQuery＝R&select＝

Hello LOyid, I had to create a new account to create the project to predict prices with time series The new project is name : Algorithm for Predict price with Time Series Thanks

Algorithm for Predict price with Time Series

Stock Market Multitask - Multi-users iOS App STAGE 1: Price Prediction Based on Previous Days Values ======================================================= Will be an iOS for 5 users. Using historical data from previous dates, from Yahoo Finance, predict the next 5 DAYS Max/Min value of any "Stock Symbol" ("SS"). There will be a list of "SS" of interest for each user. You have to provide predictions for all of them. Samples values for some "SS" of interest are in pictures A, B, C & D. Values shown are Minimum, Maximum & Closing: - USD/MXN - $19.7192 / $19.8690 / $19.8042 Historical values can be found here: - USD/MXN - STAGE 2: Handle yours, and another 5-7 predictions ================================================== Along

...a model to predict the app rating, with other information about the app provided. Problem Statement: Google Play Store team is about to launch a new feature wherein, certain apps that are promising, are boosted in visibility. The boost will manifest in multiple ways including higher priority in recommendations sections (“Similar apps”, “You might also like”, “New and updated games”). These will also get a boost in search results visibility.  This feature will help bring more attention to newer apps that have the potential. Domain: General Analysis to be done: The problem is to identify the apps that are going to be good for Google to promote. App ratings, which are provided by the customers, is always a great indicator of the goodness of t...

Hello, I need an agronomy freelancer to get advisory on the calculati...the calculation of soil water content for irrigation planning. I need a 2 hour session to obtain the theory needed and resolve doubts. I'm the CEO of a small company focused on agriculture data management, that delivers to agriculture producers 'when and how much water' is needed to be efficient. This is related to droughts and climate change. Now I'm developing the data calculation engine to predict the water content in crops in the next 5 days, combining three components: real time soil moisture measurements on crops, AccuWeather's weather forecast and historical and calculation of evapotranspiration. The session is a class, but a longer joint work can be projected. I speak spanish...

Only reach out if you're very familiar with building a NN using Keras to predict income of individuals. The task is straightforward with limited budget.

an assay has a CV of 15% for a standard reference, ie, the same substance tested in the same way in the same lab; all known variables have been controlled and yet there is still a CV of 15%. Supposing the assay variability applies equally to a test substance. Is it possible to estimate the chance of a false positive and a false negative from the variability for the reference and identical variability for the test substance? the reference is used to normalize results across different studies. How should I describe the risk posed by normalization to a reference that is so variable?

Need a expert who can design a IoV simulation in any tool on the trust management which can predict the trustworthiness of vehicle and that algo should also detect attacks on the vehicle.

- Understand SoftMax Regression - Understand Linear Regression, Logistic Regression Calculations - Neural Network Calculations - KN-MEANS - LogLoss - Decision Trees - Naive Bayes Classification - Evolutionary Computing - Genetic Algorithms - Python Programming and Tensorflow - Convolutional Neural Networks - SoftMax Classifier

This is a data analysis project. You may use whatever method you want to do. The goal is to use the data to predict the "cointegration_constant". X (independent data): first 16 columns of the dataset y : column "cointegration_constant" Task: "milestone_date_o2" represents the end day of a quarter. We have to use the data before a specific milestone_date_o2 to predict the cointegration constant of this milestone_date Example: For milestone_date_o2 20201231, we train the model (training data) with the data before 20201231 (20020630 to 20200930), that is row 0-19086 in the data set. The testing data is the row 19087-20591. The better the recall and precision, the better the result. Goal: You need to use a consistent model structure (like same al...

app for predict weather with all week weather in it

Create a defect detector software with tensorflow openvino or something similar. The software should go through a set of images taken from a drone of wires and with set parameters it can pick out the broken or defected wires there is already some softwares on youtube with codes but I am looking for one that can quickly sort bulk images faster

I need 10 Jupyter/Python notebooks created that will run...in Pandas with daily and Monthly charts 003 Two data series with Moving Averages (MA), RSI and MACD 004 Seasonal Arima 005 Auto-correlation and Partial Auto-correlation 006 Detailed Time Series Analysis 1 series 007 Detailed Time Series Comparison/Analysis 2 series 008 Determine if multiple input series affect one series 009 Determine if multiple series affect each other 010 Machine Learning = Tensorflow ??? 2). Please state your experience with Binder, Jupyter and why this project i 3). Please state your experience (if you have any) with "R" and Julia So the project is to create 10 analytic Python/Jupyter notebooks with detailed comments in each notebook section and get them tested and running on the Binder ...

Have a Flask app (based on tensorflow) that need to be deployed in EC2 instance. Use of tensorflow is giving me an error and hoping to get some help.

Deploying Flask Microservice to Google Cloud and Build CI/CD Pipeline with GitHub Repos. User Docker and Google Cloud. Use Python and Flask for Back-End and Use React and JavaScript for Front-End. Use TensorFlow/Keras LSTM package.

If you have the knowledge and ability to create a program that will predict who will win in basketball quarters u Are Man for thes job . For start i will need Prediction for one country : Philippines Or according to your wish, to see if it works and then we will go for more. Possibility of further cooperation in expanding the project.

I want to configure NVIDIA CUDA so that I can run a simple python script. If you have experience with configuring cuda on windows server 2019 VPS, then you are welcome. This is an urgent task. If you have experience, you can finish it within 1-2 hours. If you are proved to be a fit, I will provide anydesk id. Thanks.

Implementation Implement the Item-based collaborative filtering algorithm (please do not use any off the-shelf libraries for collaborative filtering, the ide...algorithm (please do not use any off the-shelf libraries for collaborative filtering, the idea is to implement the algorithm yourself) • Step 1: Use the ratings specified in file to learn similarity between movies. You can use the correlation coefficient (as given in the slides) or cosine similarity to compute the similarity weights. • Step 2: For each row in , predict the rating based on a neighborhood of similar movies. Refer to the slides on collaborative filtering for the equation used to compute the predicted rating. You can select any appropriate neighborhood size when making the prediction.