AutoencoderJobb

...code base and dataset I provide, construct an Abstract Syntax Tree (AST) based on them, and then create an Autoencoder using TensorFlow to compress the resulting AST for a classification problem. Key requirements for this project include: - Proficiency in Python: You will be working with a Python code base and dataset. Familiarity with Python is essential for this project. - Understanding of AST: You should be able to construct an AST tree based on the provided dataset and code base. - TensorFlow Experience: I would like the Autoencoder to be developed using TensorFlow, so prior experience with this framework is a must. - Output format: The desired output format for the Autoencoder is JSON, so you should be comfortable working with this format. This project is i...

...knowledge in the use of autoencoders for feature extraction and GRU models. Key project elements include: * Professionals will need to implement an autoencoder for feature extraction with the core goal of reducing data dimensionality. Prior experience in working with high-dimensional data and in deploying autoencoders is necessary. * Subsequently, the system built should be capable of classifying Denial of Service (DoS), User to Root (U2R), and Probe attacks within the KDD dataset. Good working knowledge of GRU models and the mentioned attacks is required. * The project includes steps to ensure the accuracy and reliability of both the autoencoder and GRU model. This includes: 1. Hyperparameter Tuning: Applicant should be dexterous in practicing various optimization te...

The main task is to create the variational autoencoder.

For this project, I'm seeking a skilled machine learning engineer with proficiency using TensorFlow jupyter notebook to create a variational autoencoder model. Your task will be to detect anomalies related to mobility patterns within an Excel-format dataset. Key Tasks Include: - Analyzing a large dataset with more then am million rows and 32 columns. - Building a variational autoencoder in TensorFlow specifically designed to identify anomalies in mobility patterns. -Visualize the result between two time Ideal skills and experience: - Proficient in TensorFlow and machine learning algorithms. - Experience with variational autoencoder . - Demonstrable expertise in anomaly detection algorithms, particularly in mobility patterns data.

I need a skilled freelancer to tackle a specific issue I'm encountering with my autoencoder model in python on the Colab platform. **My Requirements:** - Diagnose and resolve training issues. - Experience with large image data handling. **Skills and Experience Needed:** - Proficiency in Machine Learning and Neural Networks. - Hands-on experience with autoencoders, particularly with image data. - Familiarity with the Colab environment. - Strong problem-solving and analytical skills. The ideal candidate should clearly understand the typical challenges faced while dealing with autoencoders and have a proven record of fixing similar issues. If you have worked on similar tasks and have a knack for ironing out computational wrinkles, I would love to work with you.

I need a skilled freelancer to tackle a specific issue I'm encountering with my autoencoder model in python on the Colab platform. **My Requirements:** - Diagnose and resolve training issues. - Experience with large image data handling. **Skills and Experience Needed:** - Proficiency in Machine Learning and Neural Networks. - Hands-on experience with autoencoders, particularly with image data. - Familiarity with the Colab environment. - Strong problem-solving and analytical skills. The ideal candidate should clearly understand the typical challenges faced while dealing with autoencoders and have a proven record of fixing similar issues. If you have worked on similar tasks and have a knack for ironing out computational wrinkles, I would love to work with you.

I need a skilled freelancer to tackle a specific issue I'm encountering with my autoencoder model in python on the Colab platform. **My Requirements:** - Diagnose and resolve training issues. - Experience with large image data handling. **Skills and Experience Needed:** - Proficiency in Machine Learning and Neural Networks. - Hands-on experience with autoencoders, particularly with image data. - Familiarity with the Colab environment. - Strong problem-solving and analytical skills. The ideal candidate should clearly understand the typical challenges faced while dealing with autoencoders and have a proven record of fixing similar issues. If you have worked on similar tasks and have a knack for ironing out computational wrinkles, I would love to work with you.

This example shows how to train a deep learning variational autoencoder (VAE) to generate images. Ideal Skills and Experience: - Proficiency in MATLAB and experience with training Variational Autoencoders. - Strong understanding of image processing and deep learning techniques. - Ability to work with different input and output image sizes. - Familiarity with generating images using VAEs. If you are confident in your MATLAB skills and have experience with VAEs, please submit your proposal for this project.

...skilled Python developer to modify an existing autoencoder code. The code is written in Python and requires changes in the input of the autoencoder. Skills and Experience: - Proficiency in Python programming language - Strong understanding of autoencoders and their implementation - Experience in modifying existing code and making necessary changes - Familiarity with data preprocessing and manipulation techniques in Python Tasks: - Modify the input of the autoencoder code to meet the project requirements - Ensure the code runs efficiently and effectively - Debug and fix any issues that may arise during the modification process - Implement any additional features or improvements as needed Deliverables: - Updated Python code with the modified autoencoder inpu...

...changing market conditions Hybrid algorithm: RL algorithm: Deep Q-networks (DQNs) DL algorithm: Autoencoder EA: Neuroevolution Explanation: The RL algorithm would be used to learn a trading strategy that can adapt to changing market conditions. The autoencoder would be used to learn a compressed representation of market data. The neuroevolution algorithm would be used to evolve the RL algorithm to adapt to changing market conditions. The RL algorithm would be trained on a dataset of historical market data. The RL algorithm would learn to make trading decisions that are based on the current market conditions. The autoencoder would be trained on the same dataset of historical market data. The autoencoder would learn a compressed representation of market dat...

I am looking for a skilled freelancer who can develop an autoencoder dimensionality reduction code with good accuracy using Python. I have a code for PCA for dimensionality reduction on the same dataset I need a autoencoder code with fine-tuning which gives good accuracy. Dataset: I will provide the specific dataset that should be used for testing the code. Dataset is divided into 6 files 6 files needs to be dimensionality reduction using autoencoder Don't worry I have sample for that Accuracy: The desired accuracy level for the model is 80-90%. Ideal Skills and Experience: - Strong proficiency in Python programming language - Experience with developing autoencoder algorithms for dimensionality reduction - Knowledge of machine learning and deep...

Looking for help to complete the Project: (Communication Systems) In the ref paper shared for local model training they have used a unsupervised deep learning model called AMCNN LSTM , but we are planning to use supervised deep Learning model called Adversarial autoencoder, and the aggregation algorithm for federated learning used here is Fedavg, but we need FedProx and the gradient compression scheme mentioned in the same paper also needs to be implemented along with it .The datasets that needs to be used are 1) NF-TON-IOT dataset 2) NF-BOT-IOT dataset Reference document and more details on the Project are attached.

Hello, My name is Lucas, I am French and Data Scientist. I am looking for someone to do a deep learning study/model for me/with me. An autoencoder that will forecast a time series previously transformed into an image. You will find all the details in the attached document. Looking forward to discuss with you. Lucas

I am looking for a freelancer who can help me parallelise the training using autoencoders. Currently, I have a code written in Python, that works with any type of autoencoder – variational autoencoder (VAE), denoising autoencoder, or sparse autoencoder. The current code has 1-2 layers. I prefer to work on the model with the same layers, but if some further layers are required, I am open to discuss my project requirements. Furthermore, scalable and well-tested code is mandatory. To make this project successful, I need someone who has wide knowledge and experience of using different autoencoders, can work within time deadlines, and can handle tasks related to parallelizing models.

The objective is to build a system based on AutoEncoder to extract features from speech dataset for biometric access control

The research needs only review and working on certain notes. The objectives of the research are: • To filter and remove noise while preserving important features for improving the quality of low-resolution images by using novel filtering techniques. • To extract the most important facial features from the low-resolution images using Deep Autoencoder (DAE) for improved classification accuracy. • To design a low-resolution facial recognition model using a deep learning classifier NO AGENT MESSAGING PLEASE - WON'T RESPOND

Need to perform different SVM methods on a given dataset and use an autoencoder in another dataset. Need to use jupyter notebook.

I'm going to use a varational autoencoder to upload videos and sounds and then combine them all in one simple interface

1) Examine this implementation : 2)Swap CNN layers with CLTSM layers. () 3) Made CLSTM Autoencoder 4)Give input video, output gives dehazed video. Example Dataset in attachment. PLS write XX your at the beginning of the bid. So I can understand this is not AUTO-BID!

1) Examine this implementation : 2)Swap CNN layers with CLTSM layers. 3) Made CLSTM Autoencoder 4)Give input video, output gives dehazed video. Example Dataset in attachment. PLS write XX your at the beginning of the bid. So I can understand this is not AUTO-BID!

I have deep learning model for clustering, it does the clustering on data embedding learned by an Autoencoder. The problem is the AE weights seemed to be not effected by the clustering model being trained. This means that; I change the loss objective of the clustering model and it is still the "exact" same performance results (accuracy, precision, recall, F1 score) as with the previous loss objective.

Develop a CNN autoencoder for Image Analysis

I need a machine learning expert to change the autoencoder code of one file to reinforcements code. I have 3 more tasks after this. file: in

I need a ml expert to implement the autoencoder based on reinforcements for fibre optical communication. source code:

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The aim of this research project is to study and analyze the factors affecting the criticality of COVID-19 patients, and accurately predict the mortalit...COVID-19 patients, and accurately predict the mortality rate of the patients ahead of time. In this paper, COVID-19 data from the National Center for Data of Health which consists of data from 2019 to 2022. Different visualization techniques were used to extract patterns from the demographic and the clinical data of patients to determine the factors affecting COVID-19 patients. Random Forest and Autoencoder neural networks were trained to predict the mortality rate of the patients. Predictions were evaluated using AUC, ROC and accuracy scores. Neural Network resulted in an accuracy of 71.10% and Random Forest gave an accuracy of a...

Hello, I'm looking for an expert in deep learning especially the autoencoders to HELP me analyze some data(ionique images). The objectif is to find the most relevent ions and its parameters m/z. Each one of the files here(just a part of the data) is 1 ionique image, if we can fusion them all to have one complete image and then we can proceed to the analyzis with an autoencoder. It's my internship, so i need someone that will be open to have some videos calls to well discuss about the work.

Main Task - Image Reconstruction 1. Custom image Dataset must be loaded from the local drive. As of now you can load your own dataset. 2. Image Augmentation have to be included Important: There should not be any restriction on the dimensions of the image. It will work with all the image dimensions Step 1: 3. The various types of Autoencoder techniques and GAN must compared with and without hyper parameter tuning, Ensembling methods with various performance metrics. 4. There is an option to mention and control the types of noises with the range 5. The reconstructed results will be checked with the bunch of images or else the single image with similarity score with the input image.

Objective: Automate classification & verification of types of water leak in water distribution network with high accuracy Background: Local water operators with old-aged pipeline for their water distribution ne...for AI: > Historical Data of Acoustic Raw Data (6 months) > Historical On-Site Verification records (6 months) Expected Result: Minimum Viable Product (MVP) / Prototype that completes the objective in 2 months form agreed date What we're looking for: Someone with completed past development projects that heavily uses Machine Learning (Artificial Neural Network (ANN), Random Forest (RF), Autoencoder Neural (AE) Network, etc) About Rivil: Rivil is a Malaysian Water Technology company with the mission to accelerate access to freshwater at affordable co...

... and Objectives Study current unsupervised machine learning techniques, particularly state-ofthe-art on fraud detection • Investigate how transaction features cane be extracted to differentiate normal and abnormal cases • Investigate unsupervised machine learning techniques based on clustering and autoencoder to model normal transaction. • Implement an anomaly detection mechanism that using the previous model can detect outliers as potential fraud cases • Evaluate the performance of the proposed system and compare it against the state of the art in the field using standard datasets and appropriate metrics Skills This project is best suited to a person with an interest in Deep learning and strong

Labeling of the already generated univariate dataset (based on types of anomalies) Train deep learning model (except CNN) for univariate anomaly detection. (Preferred hybrid methods like autoencoder, lstm-ad, lstm-vae, mscred) Protocol the evaluation metrics such as f1-score and computational time. Also evaluate the precision, f1 score and computational time of the database with an existing repositories () containing the different neural network algorithms

...teacher asked me to do the following: autoencoders to differentiate between anomaly and normal condition taking a time window of size W. autoencoders using a pre-trained network as an encoder to differentiate between anomaly and normal condition by taking a time window of size W. I have already done part of it but I am blocked. I already made a datagenerator and have fed it to an autoencoder but the results seems really bad. I will also attached the .ipynb file so you can have a look at it. I would need not only the answer but also an explanation of how you have done it in case you accept it. THANKS. please also find the datasets here: training: Test: Validation:

Deep learning based Autoencoder to find bit error rate verses signal noise ratio using rician channel

Autoencoder to find bit error rate verses signal noise ratio using rician channel

Apply an Autoencoder to denoise real-world time-series data from inertial sensors and near-infrared (NIR) sensors for robotics.

Objective ----------------------------------- 1) Correct the error in and run to see final results 2) Create a custom fit or trainer model for Textvae model 3) Correct the error in and run to see final results 4) Create a custom fit or trainer model for FNET also.

Objective ----------------------------------- 1) Correct the error in and run to see final results 2) Create a custom fit or trainer model for Textvae model 3) Correct the error in and run to see final results 4) Create a custom fit or trainer model for FNET also.

This project aims to apply an Autoencoder to denoise real-world time-series data from inertial sensors and near-infrared (NIR) sensors for robotics.

Given a dataset that contains images of retina you have to extract the features from images using pretrain architecture (Google Net, Alexnet, ResNet) . And integrate the features extracted by all three architecture. Divide the features set as train and test. And on train dataset used sparse autoencoder for features reduction to subset. And classify it in stages

I am trying to predict the Lift to drag ratio of the airfoil using the encoder part of the variational autoencoder, as previously it was predicted using CNN. I made some changes in the code to make it a variational autoencoder but a warning appears which states that "UserWarning: Using a target size (([50, 1])) that is different to the input size (([50, 720])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size." Anyone who can help me in removing this warning and running the complete code of VAE? Link to Data:

Develop a neural network using CNN, RNN or Autoencoder to train a model for classifying a JIRA issue into a 'type' - (using other details about the issue such as summary/description). To be developed using PyTorch necessarily.

ou need to implement the following case: Input to AE 1: x1, Input to AE 2: x2 signal 1= x1+noise+x2 signal 2 = x2+noise+x1 Autoencoder 1 should decode x1 successfully. For that you need to extract crossentropy loss , and build a weight fucntion alpha + loss1/loss1+loss 2. Train both AEs jointly and assign the weigh to loss of both such that L1*alpha+L2*1-alpha. so that AE when alpha is 1 AE 1 learns to decode x1 and when alpha is 0 Ae 2 learns to decode x2. Rest we will discuss if you are interested in the project,

We need to build a "Mouse Dynamics Based Bot Detection Model Using an Unsupervised Anomaly Detection Method - Multivariate Time Series through LSTM Autoencoder" A set of data has been collected from users' mouse movement behavior on a website including (Session, Timestamp, Button, Event Type, State, x and y coordinates, Speed). Besides, there is a program that generates automated mouse movement on the designated website. Considering that, the main tasks in this project are as follows: *Preprocess the data *Extract couples of features from data including Acceleration, Mouse action duration/elapsed time, Movement speed in X and Y directions, Directional features (angle of movement)- The direction of movement at a given timestamp *There probably exists some ‘lo...

The issues in each project have similar format with fields. For example, the type of Hadoop-15502 is “bug” while the type for issue Hadoop-17830 is “improvement” . The purpose of this assignment is to use neural networks (NNs) to assign a type (such as bug, improvement, sub- task) to an issue.

Building and training Stacked LSTM Autoencoder model using a multivariate dataset to predict an output. The code should be written with Pytorch library, not Keras.

Building and training Stacked LSTM Autoencoder model using a multivariate dataset to predict an output. The code should be written with Pytorch library, not Keras.

I need a autoencoder neural netwrok .I can send details of networks and be in Python

Autoencoder of Dataset for extraction

autoencoder and GAN for image sequence and normalizing flow SR

Objective and Deliverables 1) Write a one page each review of each document in the Autoencoder (AE) and Variational Autoencoder (VAE) folder. 40 pages for VAE and 4 pages for AE. All contained in one pdf document. 2) Each page should be double column consisting of the following A) Check first if the problem applies for Natural Lanaguage not only for images. Mention this in the context if not. A) Describe the context of the research B) Objective of the research and short result report C) The loss function and description of the variables and symbols used. D) Conclusion on the outcome of the method E) Source code (pytorch or tensorflow) of the project 3) Deliverables include latex pdf, latex source and of the original reference of all 44 papers in the folder. Ple...