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    1,087 keras jobs found, pricing in INR

    Machine Learning should be used to test the game "Dirt 5." There should be 10 - 15 mins of continuous input where the car will try to stay on the tracks and go around the tracks. The goal is not to win the game but to simulate someone playing the game. If the car hits a barrier or another car or gets off track of the course, it correct itself and get back on track. OpenCV, TensorFlow and Keras should be used. No other deep learning languages. The requirements listed are I’ll of the requirements. Other than that I am open. You can look at how the game is played on YouTube for more details: 1 Example: Here is an example video of another game Deliverables Must be in the languages mentioned. Must have continuous input for 10-15

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    SET ME UP A PYTHON WEB SERVER 9 hours left
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    I would like to access via a simple URL (ex. ) ,anywhere, to this configured server (ex. windows server 2022), that can support Keras and PyTorch libraries. The final goal is this one: with a simple URL (ex. ), that i can launch for example from another website written in PHP, i would like to launch a python algorithm that will send back some results. I WILL PAY YOU AND THE CLOUD. WE'LL DECIDE TOGETHER THE CLOUD, I PAY IT AND THEN YOU WILL CONFIGURE IT FOR ME.

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    implementing the project in Python using CNN model using keras deep learning framework.

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    ...autonomously! Machine Learning should be used to play the game "Dirt 5." There should be 10 - 15 mins of continuous input where the car will try to stay on the tracks and go around the tracks. The goal is not to win the game but to simulate someone playing the game. If the car hits a barrier or another car or gets off track of the course, it correct itself and get back on track. OpenCV, TensorFlow and Keras should be used. No other deep learning languages. The requirements listed are all of the requirements. Other than that I am open. I believe the game is free for PC on steam. You can look at how the game is played on YouTube for more details: 1 Example: Here is an example video of another game being trained to play autonomously

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    implementation to C++(qt)/tensorflow (v2) and keras -- 2 Please message for further information 10AUD - 40AUD

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    Machine Learning should be used to test the game "Dirt 5." There should be 10 - 15 mins of continuous input where the car will try to stay on the tracks and go around the tracks. The goal is not to win the game but to simulate someone playing the game. If the car hits a barrier or another car or gets off track of the course, it correct itself and get back on track. OpenCV, TensorFlow and Keras should be used. No other deep learning languages.

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    ...do it for all main Deep NN packages , but do not use hidden layers LINEAR regression MEANS NO hidden layers (upper + lower ) / 2 performance should be not worse than should work not slower than keras for example point to start tensor flow pytorch then it should be 4 solutions, all of them should provide good performance each solution in separate file data is mixture of categorical and continues features at least 3 categorical features at least 3 continues features at least 10000 rows you find needed data sets by yourself : several datasets ( more than 2) all data

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    I have can model, in the pre-processing part I read train data from folder and val data from another folder. I need to change them and read all data from one folder and random split the data into train and val. it is build on python, keras and tensor flow

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    do it for all main Deep NN packages , but do not use hidden layers LINEAR regression MEANS NO hidden layers keras for example point to start tensor flow pytorch then it should be 4 solutions, all of them should provide good performance each solution in separate file data is mixture of categorical and continues features at least 3 categorical features at least 3 continues features at least 10000 rows you find needed data sets by yourself : several datasets ( more than 2) all data sets used for all models see primitive linear (or log-linear) model W cdot mathbf{x} + mathbf{b} (where W is a weight matrix, mathbf{x} = (x_1, x_2) is an input vector, and mathbf{b} is a bias vector) without hidden layers,

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    time - 1 week python code for quantile Ridge LINEAR regression by using Deep Neural Networks packages do it for all main Deep NN packages , but do not use hidden layers keras for example point to start tensor flow pytorch then it should be 4 solutions, all of them should provide good performance each solution in separate file data is mixture of categorical and continues features at least 3 categorical features at least 3 continues features at least 10000 rows you find needed data sets by yourself : several datasets ( more than 2) all data sets used for all models see primitive linear (or log-linear) model W cdot mathbf{x} + mathbf{b} (where W is a weight matrix, mathbf{x} = (x_1, x_2) is an input vector, and mathbf{b} is

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    I use keras engine with tensorflow to make a model for Mlkit (which doesn’t support hebrew)

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    Any experience with ocr? I use keras engine with tensorflow to make a model for Mlkit (which doesn’t support hebrew)

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    Hello, I need someone who can reimplement/translate a small Python/Tensorflow command line app implementing a training based on data from a text file written with the Tensorflow 1 API and numpy into a Qt/C++ command line implementation using Tensorflow v2 and if needed / easier also Keras. Currently the implementation generates text files storing the trained weights and biases, this shall be replaced by storing the entire model in a Tensorflow HDF5 format that can later on be used by another Tensorflow inference implementation. The python implementation can be already found as an attachment. Have a look so you can get a better understanding if you can handle the job and how much work it will be for you. Note: The implementation of the Adam Optimizer shall be entirely replaced by the...

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    ...coef_lower_ where coef_upper_ coef_lower_ like coef_ : array of shape (n_features,) Estimated coefficients for the features -------------------------------------------------------------------------- do it for all main Deep NN packages , but do not use hidden layers keras for example point to start tensor flow pytorch then it should be 4 solutions, all of them should provide good performance each solution in separate file data is mixture of categorical and continues features at least 3 categorical features at least 3 continues features at least 5000 rows data you find : several datasets ( more than 2) see primitive linear (or log-linear)

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    5 years Experience querying databases and using statistical computer languages: R, Python, SQL, etc. Experience using web services: Redshift, S3, Spark, DigitalOcean, etc. Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modelling, clustering, decision trees, neural networks, etc. Experience with distrib... S3, Spark, DigitalOcean, etc. Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modelling, clustering, decision trees, neural networks, etc. Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc. Should know the following frameworks: TensorFlow, PyTorch, Caffe, mxnet, Keras an...

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    do it for all main Deep NN packages , but do not use hidden layers LINEAR regression MEANS NO hidden layers keras for example point to start tensor flow pytorch then it should be 4 solutions, all of them should provide good performance each solution in separate file data is mixture of categorical and continues features at least 3 categorical features at least 3 continues features at least 10000 rows you find needed data sets by yourself : several datasets ( more than 2) all data sets used for all models see primitive linear (or log-linear) model W cdot mathbf{x} + mathbf{b} (where W is a weight matrix, mathbf{x} = (x_1, x_2) is an input vector, and mathbf{b} is a bias vector) without hidden layers,

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    # Abstract - Create PowerPoint document contains slides at least 10 pages - Write down speaker scripts on each pages in "Speaker notes" - Speaker notes must be written at least 1500 words # Content details - Target: ML advance ~ experts - Example: programming methods, model architecture, project management, etc... - Library usage: Keras, Pandas, Scikit-learn # Deadline 2 weeks after contract. # Budget 15USD per document. # Copyright We will get all copyrights on results this project.

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    do it for all main Deep NN packages keras tensor flow pytorch then it should be 4 solutions, all of them should provide good performance data is mixture of categorical and continues features at least 5 categorical features at least 5 continues features at least 100000 rows data you find : several datasets ( more than 2) see primitive linear (or log-linear) model W cdot mathbf{x} + mathbf{b} (where W is a weight matrix, mathbf{x} = (x_1, x_2) is an input vector, and mathbf{b} is a bias vector) without hidden layers, better to find and use existing code from web to prove all done correctly: make prediction manually use only one dimension vector coefficients like https://scikit-learn

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    Proficient with object-oriented and scripting languages: Python Familiarity in building and deploying machine learning models Familiarity in DL frameworks like Keras and Tensorflow. Experience in tuning ML models. Design and implement machine learning, information extraction, probabilistic Kafka,Storm will be a plus. Time Series will be a plus.

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    Dear all I am looking for someone who can complete an assignment for my data science class. You should build a CNN to classify handwritten digits from a given data set using tensorflow or keras. An accuracy of 97% is needed. A skeleton code is provided. Furthermore, I got the solution from last year but it needs some changes (of course I provide the completed and accepted solution from last year). Current skeleton code can be found in the appendix. much appreciated your help

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    Introduction I need a video analysis ML model building in Google Colab that I can re-use to analyse my own videos. This model needs to produce key points on videos of horses walking. Based on training data that will be provided and is pre-labelled. Deeplabcut Model Zoo First you should get yourself familiar with the DeepLabCut Model Zoo here - DLC Model Zoo Use the “Horse Side View” model and analyse this video below: Horse Video 1 Notice in the outputs that the model zoo outputs a key point video, scattergram and csv file. I need all 3 outputs from my model also. Read here for more information on the DLC Model Zoo Horse10 model Then I need a model training on some pre-labelled data. This should be a combination of the model zoo above and the data contained in the H...

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    I need help to Training resnet with different image categories, using Keras Python. I will provide more detail in chat.

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    Require future predictions based on historical time series data. Require the training of the model and provision of a trained model that can be effectively relearnt with transfer learning. Require the predictions for 1 hour, 3 hrs, 6 hours out. Looking at potentially a LSTM through Keras. Require the confidence intervals / min/max bands for prediction to also be made available. Require the model to be trained via GPU. Dataset approx 1M rows and 50 columns wide

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    Migrate existing code packages 4 files of 6. Code is written in Tensorflow 1.14 version (Python 3.6) with low level TensorFlow APIs, need to migrate to TensorFlow 2.7.0 (Python 3.8) using Keras and Functional API for Model layers. Each file for migration is in the following code line lengths: file1: 128lines, file2 139lines, file3 104lines, file4 105lines. After migrating, code can`t use any endpoints to access placeholders, sessions, collections and other TensorFlow 1.x-style functionality.

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    This project aims to apply some suitable AI/ML methods to search the internet, the images in particular, and other multi-media materials, such as videos if possible, to retrieve the truly relevant ones, and to extract and classify them into some predefined categories. i need a project something related to this: or something related to this: in the above two projects, classification of weather or weather prediction took place, but I need classification of climate events. a search algorithm to find the images of climate event has to be chosen and then classification model has to be built.

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    Phython and Keras help to implement Neural Networks Based AI Project

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    Develop an end-to-end deep learning-based image classification model for datasets like CIFAR 10, CIFAR100, MNIST, FMNIST, etc. Use an implementation of CNN model on Python with Keras or TensorFlow and transfer learning to recognize simple scenarios like CAT, DOG for our purpose object1,2,3 Project have to be based on Nvidia Jetson sample or demo or tutorial or blog for example: 4.1. NVidia Transfer learning 4.1a. 4.1b. 4.2. training-custom-pretrained-models-using-tlt/

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    Neural Nets Based AI Project help using Python and Keras

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    AI project (neural nets) Implementation using Python and Keras

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    Help with implementing AI project (neural nets) using Keras and Python"

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    Use an implementation of Mask R-CNN model on Python 3, Keras, and TensorFlow

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    Need help in fixing code for Tabular Generative Adversarial Network (GAN). The code is python based and leverages keras library. Candidate must have good technical skills in Python, Machine Learning, Keras, Tensorflow, GAN. GAN skills is must.

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    Machine Learning development with python using multiple frameworks(Django, Flask) and libraries (TensorFlow, Scikit-Learn, OpenCV, NLTK). Progr...NLTK). Programming Language Python, R, SQL, Java, C, JavaScript Key Skills ● Software Development ● Machine Learning ● Deep Learning ● Predictive Analysis ● Data Visualisation ● Statistical Modelling ● Data Analytics ● Data Mining ● Quantitative analysis ● Model Development ● Cloud Computing TOOLS: Flask, Django, AWS, Google Cloud, MySQL, PostgreSQL, Airflow, Tableau, Apache Spark Deep Learning :Keras, NLTK, OpenCV Data Mining NumPy, Pandas, Sklearn, SQL. Statistics/Machine Learning Statistical Analysis, Hypothesis Testing, Linear/Logistic Regression, Clustering, Classification. Data Visualisation Tableau, Seaborn, Matplotlib

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    Dear Freelancer, Please bid, if you are an expert in Tensorflow, Pytorch, keras and NLP, This project is about author style transfter using GANs such as WGAN, cycleGan etc.

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    Take an MNIST dataset, train the network using Keras (or another program), and then o visualize the features in the neurons at different layers o See how removing neurons at different parts of the network impacts performance — maybe combined with the first?

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    I need some help with data science project which has Keras, TensorFlow, Skikit Learn and Seaborn

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    Hi, I'm currently learning Python and Keras! I tried to program a few things, but I got stuck and need someone to fix my code and tell me what I did wrong (e-mail communication is enough). If you have any questions please let me know.

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    Need to create Python3 program that using ML(Keras-Lib) to create an FNN Training Module

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    ..."lying around" ML scripts from GitHub to MLReef. To make them easly useful to our users while still keeping flexibilty and control. What is the task? 1. Find a ML function of your liking (can be as small as a data-preprocessing opertation to the newest Deel Learning Algorithm) and publish it successfully in MLReef. Take for example: GitHub: MLReef: There is a video tutorial on how to port a model. The fast explanation: you need to write decorators based on parameter arguments set by e.g. argparse or any other command line addressable argument function. 2. Show that it worked (via an experiment or data preprocessing or visualization). We will support you during the entire process. Depending on

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    Chatbot using python you can use TensorFlow and keras not Pytorch. I only need python file also code should not have any plagiarism and able to chat. PYTHON and use Reinforcement Learning

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    need someone who can check neural network program done in Python using Keras, Sequential Dense

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    Designing solutions for real world problems that is interesting. Scikit-learn, Keras, TensorFlow 2.0 Has some components - please message. Jupyter Notebook of preference.

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    ...highest accuracy form [16, 32 or 64] possible hidden unites sizes. The method should return the best model that will be used for prediction. Plot the loss of your model against each epochs. Hint: you can use keras or sklearn library. b) deftrain(train_x,train_y):best_model This method should build and train your selected model to capture data features. Perform 10-fold cross validation to select the best learning rate from the set [1e-2, 1e-3, 1e-4]. The method should return the best model that will be used for prediction. Plot the loss of your model against each epochs. Hint: you can use keras or sklearn library. c) def predict_evalute (test_X,test_Y model): • Predict the target for the test data. • Evaluate the model on test data by calculating accuracy, F1 scor...

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    I have a working puthon code related to a face emposion detection using python tensorflow and keras, it is already written and worked on other laptop but I could not run it on my labtop. I need some guidence to make the code run.

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    I need some quick support on checking errors and debugging a Keras based Tensorflow model.

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    NDA
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    ...highest accuracy form [16, 32 or 64] possible hidden unites sizes. The method should return the best model that will be used for prediction. Plot the loss of your model against each epochs. Hint: you can use keras or sklearn library. b) deftrain(train_x,train_y):best_model This method should build and train your selected model to capture data features. Perform 10-fold cross validation to select the best learning rate from the set [1e-2, 1e-3, 1e-4]. The method should return the best model that will be used for prediction. Plot the loss of your model against each epochs. Hint: you can use keras or sklearn library. c) def predict_evalute (test_X,test_Y model): • Predict the target for the test data. • Evaluate the model on test data by calculating accuracy, F1 scor...

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    I need python code using Keras and tensorflow to train a python sequence to sequence model in the following form def create_sequence_to_sequence_model(X_train, X_test, y_train, y_test) it should return the cross entropy score or similar please provide, 1. RNN 2. LSTM 3. Attention model

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    Hello geek, I'm having a dataset of 500 images(2 classes) and wanted to do image classification. I would like to see this dataset trained on multiple ML&DL Algorithms using KERAS or Sci-kit Learn in Python. Algorithms may include SVM, Random Forest, custom-CNN, MobileNet, Efficentdet-D7, or anything you are comfortable with. What else you could provide: 1. Basic performance Visualizations(Train, Validation acc/loss plots) 2. Evaluation metrics (Acc, Precision, Recall, F1 score, Confusion Matrix) 3. Comparing which model is performing better. Additional benefit: On successful completion, you may be offered the opportunity to work on more similar datasets and get paid per project.

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