Dcgan mnist jobs
TASK 1 Create an object detection model using R-CNN, Yolo, or SSD. Since there's no small dataset such as MNIST and Fashion-MNIST in object detection, you can use the dataset from the d2l/notebooks/mxnet/chapter_computer-vision/ notebook. The dataset has 1000 banana images with different rotations and sizes placed randomly on some background images. 1.A Obtain the dataset. Working on a small dataset such as the one from the would allow you to train and test different models without taking hours/days to finish. Go to the notebook and obtain the dataset. 1.B Build an object detector. • using YOLO, and SSD. (need two separate notebooks) • Using the dataset, train, or fine-tune (a pre-trained model) to create an object detector
I need support in cleaning and organizing a dataset. - You should be proficient in Python. - Experience with data cleaning and manipulation. - Familiarity with data analysis would be an added advantage. Part A: Advanced Data...Compute Pearson's correlation coefficient for all attributes, save as corr_matrix. Sort and display correlation of all attributes with 'number_of_reviews'. Visualize with Seaborn's heatmap for corr_matrix and pandas' scatter_matrix for 'number_of_reviews', 'reviews_per_month', 'availability_365'. Part B: PCA with MNIST Dataset Discuss motivations and drawbacks of reducing dimensionality, including the curse of dimensionality. Explain if and how you can reverse PCA. Apply PCA on MNIST with a 95% e...
You have to do Section B and Section C of the Assignmnet i.e. -Section B (Scratch Implementation) : Neural Network Implementation -Section C (Algorithm implementation using packages) : Multi-Layer Perceptron (MLP) Implementation on SVHN Dataset and do all their subparts accordingly
I want to make a air canvas, where a user can write on the screen using hand gestures, the project should also have a virtual keyboard. The project should be made using mnist, emnist datasets, deep learning algorithms like cnn, ensemble, hybrid models. The output should also include convolution matrix, and other details as well. The project should be made as a website.
I am looking for a skilled ML developer who can implement a GAN model for my academic research project. The specific GAN model I am looking to implement is a GAN for chest X-rays, with a focus on overcoming class imbalance and data augmentation. Skills and experience required: - Strong understanding and experience with GAN models, specifically DCGAN, WGAN, or CGAN - Experience working with medical imaging datasets, particularly chest X-rays - Proficiency in data preprocessing techniques to handle class imbalance and generate synthetic data - Familiarity with machine learning frameworks such as TensorFlow or PyTorch - Knowledge of evaluation metrics for GAN models in the medical domain The project will involve using an existing dataset for training the GAN model, which I already ha...
...expert to assist me with my project. Here are the details: Specific Dataset: - You will get a 2 sample files that are 1GB in size Preferred Machine Learning Model: - You have to convert the current python script used to the new usecase. The new usecase and documentation will be delivered on accepting the challenge. - Attached you will find a sample of the custom model that is used for mnist. We will NOT use mnist, this is a example so you know which script you would have to edit for the new task. Everything has to be changed to Tinygrad Tensor calculations instead of numpy. In this way we can run all action on the GPU. Desired Outcome: - You provide 2 scripts (all abreviated from my script) but all changed to tinygrad python library. -- Script 1: --- You create the spe...
I am looking for a freelancer to help me with a machine learning task from scratch. The desired outcome of the task is classification. I already have a dataset, which is Mnist.
I am looking for a skilled freelancer who can generate synthetic text data using GAN for the purpose of training a model. The ideal candidate should have experience in working with GANs and should be familiar with different architectures such as DCGAN and CycleGAN. However, since I have no preference for a specific architecture, the freelancer can choose the most suitable one for the task. The main goal of this project is to generate high-quality synthetic text data that can be used for training a model.
...clustering. The primary task involves implementing the LSH algorithm for d-dimensional vectors and vector clustering techniques on the MNIST dataset of handwritten numeric digits. -ANALYTICAL REQUIREMENTS DOCX WILL BE PROVIDED IN CHAT -A code is available but needs to read and output different things and that changes many .c files. Key Deliverables: Implement the LSH algorithm based on the Euclidean (L2) metric. Implement the random projection algorithm on the hypercube for the L2 metric. Implement vector clustering techniques using the k-Means++ initialization and MacQueen method for updating. Parse and handle input from binary files containing the MNIST dataset. Generate output files detailing search and clustering results. Ensure modular and documented code with a c...
...clustering. The primary task involves implementing the LSH algorithm for d-dimensional vectors and vector clustering techniques on the MNIST dataset of handwritten numeric digits. -ANALYTICAL REQUIREMENTS DOCX WILL BE PROVIDED IN CHAT -A code is available but needs to read and output different things and that changes many .c files. Key Deliverables: Implement the LSH algorithm based on the Euclidean (L2) metric. Implement the random projection algorithm on the hypercube for the L2 metric. Implement vector clustering techniques using the k-Means++ initialization and MacQueen method for updating. Parse and handle input from binary files containing the MNIST dataset. Generate output files detailing search and clustering results. Ensure modular and documented code with a c...
...particularly in the domains of vector search and clustering. The primary task involves implementing the LSH algorithm for d-dimensional vectors and vector clustering techniques on the MNIST dataset of handwritten numeric digits. ANALYTICAL REQUIREMENTS DOCX WILL BE PROVIDED IN CHAT Key Deliverables: Implement the LSH algorithm based on the Euclidean (L2) metric. Implement the random projection algorithm on the hypercube for the L2 metric. Implement vector clustering techniques using the k-Means++ initialization and MacQueen method for updating. Parse and handle input from binary files containing the MNIST dataset. Generate output files detailing search and clustering results. Ensure modular and documented code with a comprehensive README. Provide a Makefile for easy com...
I am looking for an experienced freelancer to help me with a Machine Learning project, specifically related to image inpainting problems using a DCGAN. The goal is to make an AI model that can complete objects in images that have been partially erased or are incomplete. The desired output resolution is low, under 512x512. I don't have any specific datasets for this project, so the freelancer will have the freedom to choose which datasets to use. Regarding documentation, I already have code for the project, but need to train the model and send the results. Thank you very much in advance!
I have a paper that proposed a hybrid framework, I want the code of "a lung cancer deep convolutional GAN (LCGAN) to generate synthetic lung cancer images". It was based on DCGAN but they have some differences in the architecture. The project requests important points: 1- Write the code in Python programing language using Pytorch in Google Colaboratary 2- Explain each build layer and the details of this layer; how and why (the purpose of changing it) 3- Write explain comment for every function 4- Explain how can dwnload the images are generated from the model on my drive 5- How i can use the same code with different dataset with different size such as: 512*512 640*640 1536*2048 6- Describes the steps to run the project 7- Firstly used the same dataset in the paper (it is ...
I am looking for a sk...can help me create a GANs model for image generation. I will be providing the dataset, so no need to worry about that. The ideal candidate should have experience with GANs and deep learning in TensorFlow. The generated images should be of low resolution, less than 512x512. If you have experience with image generation using GANs in TensorFlow, I'd love to hear from you. I require three models: - DCGAN model and code (Convolution based) - Cycle GAN model and code (Convolution based) - Vision transformer GANs model and code For all three evaluation table and FID (Fréchet Inception Distance) for image quality evaluation Dataset: You can ask me for any missing details in your bid I'll update it.
I am looking for a freelancer to implement the Inception-Resnet-V2 model to detect skin cancer in images. I have already collected and labeled a dataset for training the model. The ideal candidate should have experience in model implementation using Python. Some additional requirements include: - Implementing the model on HAM 10000 dataset. - Optimizing the model for accuracy and...training the model. The ideal candidate should have experience in model implementation using Python. Some additional requirements include: - Implementing the model on HAM 10000 dataset. - Optimizing the model for accuracy and performance. (Accuracy should be more than 95%) - Providing documentation and code for future reference HAM 10000 Dataset :
...to help me by writing the needed code, following the instructions I posted in Files. The function get_mnist_data can be found below. Please send me a message if you need more information. Thank you in advance! from import fetch_openml from sklearn.model_selection import train_test_split import numpy as np import as plt def get_mnist_data(n_train): mnist=fetch_openml('mnist_784') data=() target=() X_train, X_test, y_train, y_test=train_test_split(data,target,train_size=n_train) X_train = ((X_train,([0],28,28))) X_test = ((X_test,([0],28,28))) y_train = ('uint8') y_test = ('uint8') hist1=(y_train)
Any (or all) of A- PyTorch Video Diffusion model, can be a simplified/stripped down version of one of these two codebases applied to moving MNIST. This codebase works RaMVID B- datascience / ML Dockerfile - Dummy variables for user/cluster info - Can scale number of GPUs - nvidia CUDA and cuDNN version changeable (FROM an nvidia image is fine) - conda package management - only torch necessary, but include lines with comments for other installations Stripped down/simplified Project structure should be - Dockerfile - - - - - - code - - other_files - ... Notes - All input variables must be contained in the yaml files and logically
I have 472 lines of Python code that I would like to be translated to R. Here is Python code: More details, msg.
THIS IS AN MACHINE LEARNING PROJECT BASED ON FASHION MNIST CLASSIFICATION.I AM UPLOADING MY PROJECT. PLEASE CONSIDER THIS.
MNIST Dataset SVM model .should not use any advanced packages we should do with the algorithm. You only use the Python packages included in the following cell. You are not allowed to use other advanced package or modules unless you are permitted to.
Hello, Amit this side. We already had a word regarding the project. I will send details in next messages.
...Project Name: Artificial Intelligence June Minor Project • Project Description: The goal of this project is to create a model that will be able to recognize and determine the handwritten digits from its image by using the concepts of Artificial Neural Network. Perform a digit classification to correctly identify digits from a dataset of tens of thousands of handwritten images from the MNIST dataset from keras. MNIST database of handwritten digits is used as dataset. It consists of a training set of 60,000 examples, and a test set of 10,000 examples. The digits have been size-normalized and centered in a fixed-size image of 28*28 pixels (784 pixels). also create a .hdf5 model Share your project as a .ipynb file (colab notebook)...
JavaScript function OCR for handwritten digits using the data from the MNIST database This data consists of 28x28 grayscale 0-255 images of handwritten digits. Each image is labeled with a byte having value 0-9 depending on the digit the image represents. and write function of Parser and Knn algorithm
Investigation of different optimization algorithms on MNIST database. Conditions : 1- The network must contain at least 3 hidden layers 2- Plot the output in different stages 3- Implement with Tensorflow. Finally, the results of examining these algorithms are presented in a plot such as the attachment image.
1. Google Colab code 2. Report of the results 3. Deadline 20 days after selection 4. After selection, you must send progress to avoid mistakes within 5days. 5. If it is not work in Google Colab on my Mac, you must fix it through TeamViewer or so. I. Music Dataset CSV data (fixed, I will give you and ...fix it through TeamViewer or so. I. Music Dataset CSV data (fixed, I will give you and discuss.) 2. Data analysis: (Reading and Understanding the Data, Showing raw data, Visualizing Audio Files, Plot Raw Wave Files, Spectrograms, pectral Rolloff, Spectral Centroid, MFCC (Mel-Frequency-Cepstral Coefficients), Chroma Feature, Zero Crossing Rate, Feature Extraction, Scaling the Features) 3. Build DCGAN (Deep Convolutional Generative Adversarial Network) for emotion-based genre classi...
Music Genre Classification with DCGAN Work content 1. Google Colab code 2. Report (Simple) the results 3. Deadline (10days after being selected or can be decided through talking. Should mention progress after 3-days). I. Dataset (music data) CSV data (fixed, I will give you or we can discuss) 2. Data analysis: (Reading and Understanding the Data, Showing raw data, Visualizing Audio Files, Plot Raw Wave Files, Spectrograms, pectral Rolloff, Spectral Centroid, MFCC (Mel-Frequency-Cepstral Coefficients), Chroma Feature, Zero Crossing Rate, Feature Extraction, Scaling the Features) 2. Build GRU (Gated Recurrent Unit) for genre classification 3. Model Evaluation of DCGAN for genre classification (Confusion matrix, data results, figures) 4. Comparison with CNN results (or others...
...learning library that supports distributed training, such as Tensorflow and PyTorch. You will start with writing the code for training the model on a single machine. Training should start from a random linear vector as your model. Then, calculate the loss of your model on the dataset using: , where and are training data features and label, respectively. The dataset for you to train your model is MNIST handwritten digits database. Train to minimize with an optimizer such as gradient descent. The next task is to modify the workloads so that they can be launched in a distributed way. You will experiment with both synchronous and asynchronous SGD. In distributed mode, dataset is usually spread among the VMs. On each iteration, the gradients are calculated on each worker mach...
High-quality skin cancer image generation using Generative Adversarial Networks (ACGAN). The dataset is available at and Current Project Status: The code pipeline is working properly. Code is already implemented in PyTorch. The code is written such that it also saves the progress after every epoch. Therefore, there is no tension of runtime interruptions. You can refer to Current requirements: Hyperparameter tuning or changes in the already implemented architecture is required for high-quality image generation and for reducing loss. Deliverables: 1. Edited colab code(PyTorch). 2. Results
iam building a software. using one of these dataset from the UCI Machine Learning Repository, MNIST, CIFAR, Kaggle, Public Government Datasets or elsewhere. and i need to Apply a computing model except Perceptron to the selected dataset. and i have few doubts regrading building the software
...computational speed for handwritten digit recognition process. This project mainly introduces recognition of handwritten digit using Convolutional Neural Network (CNN) based on Principal Component aim of the proposed project is to make the path toward digitalization clearer by providing high accuracy and faster computational for recognizing the handwritten digits. This project employ MNIST as data set with suitable parameters for training and testing and CNN framework for hand written digit recognition. The proposed system aims to reduce computational time significantly for training and testing and improve the efficiency of the handwritten digit recognition classification problem. Application of the project : Healthcare The findings of the proposed models should provide some i...
Create a code sample that uses PyTorch Lightning and sstack ( 3rd party library ) to train a simple model Requirements: 1. It’s allowed to use one of the existing examples provided by PyTorch Lightning from their documentation or repository (e.g. MNIST). You just need to adapt it to run with sstack. 2. The sample should allow running the training workflow without GPU, with a single GPU, and with multiple GPU. For example, you can use a sstack variable to control it. 3. Be sure to check that the training actually works in all cases (with a single GPU, with multiple GPUs, and with no GPUs). Sstack is basically allows you to automate training workflows, version and reuse data and models using a cloud vendor of your choice. We will be using AWS cloud service for this
TCreate a code sample that uses PyTorch Lightning and sstack ( 3rd party library ) to train a simple model Requirements: 1. It’s allowed to use one of the existing examples provided by PyTorch Lightning from their documentation or repository (e.g. MNIST). You just need to adapt it to run with sstack. 2. The sample should allow running the training workflow without GPU, with a single GPU, and with multiple GPU. For example, you can use a sstack variable to control it. 3. Be sure to check that the training actually works in all cases (with a single GPU, with multiple GPUs, and with no GPUs). Sstack is basically allows you to automate training workflows, version and reuse data and models using a cloud vendor of your choice. We will be using AWS cloud service for this
... More intriguing architectures that are combinations of CNN with RNN, LSTM, GRU, AutoEncoders/Decoders, GAN etc. and respective learning algorithms, as well as big databases not conventionally used in deep learning, are preferable. Traditional CNN architecture is excluded from your choice as well as more traditional databases like MNIST, CIFAR-10, CIFAR-100, Traffic Sign, the one that you currently use with CNN etc. --------------------------------------------------------------------------- YOU CAN USE THE DATASETS FROM THE LINKS PROVIDED AT THE TOP OF THE DESCRIPTION. Moreover, here's the link that I think is suitable - THE CODE IS PROVIDED IN THE
Three questions to answer To learn how to use perform classification using neural networks. To appreciate the differences in neural network architectures for the same task -- image classification -- and dataset (MNIST, CIFAR-10). To learn how to implement and evaluate deep learning models in Python, using Keras and TensorFlow. This assignment is structured in 2 parts, each using their own dataset(s). As usual, there will be some Python code to be written and questions to be answered.
convolutional neural network from scratch to classify images using PyTorch.
Hi Nguyen V., Thank you very much for your message! I would like to hire you with your proposed fee of 100€. As I described in my post, I want to implement the "Likelihood Ratio" for detecting OOD images from the paper "Likelihood Ratios for Out-of-Distribution Detection" (). As image data I want to use the MNIST database of handwritten digits. I would like to use Tensorflow as the library and Jupyter Notebook as the interface. Best regards, Mathis
I want a neural network that can be run on AWS EMR using Pyspark framwork
Time Sensitive: 4/14 11:59 PST Code a DCGAN and/or proGAN model that can take a random noise image or feature template image and generate images of dark skin conditions (Melanoma) using Keras (preferably in Google Colab too) Pricing: $150 (negotiable) Attached is the Melanoma Images (some graphic) & my current Pix2Pix GAN If you have any questions, please contact me!
...Inception Distance (FID) between 1000 generated and real data as the metric for evaluation. 3 Models for Assignment 3.1 Bayes’s Classifier with several Class Conditional Densities such as Gaussian, GMMs (Have to code up EM) 3.2 Bayes’s Classifier with different density estimates (ML, MAP and Parzen Window and nearest neighbor estimates) 5 Also using any one data set do the following:- 5.1 Train a DCGAN for one of the datasets given. Compute FID and plot the generated images in a 10×10 grids. 5.2 Train a VAE on the same dataset and repeat the above experiment. 5.3 Plot t-SNE embeddings for both 2 and 3 dimensions for one of the datasets. 5.4 Compare the above plots with the ones obtained using PCA on the same dataset on 2 and 3 dimens...
...Inception Distance (FID) between 1000 generated and real data as the metric for evaluation. 3 Models for Assignment 3.1 Bayes’s Classifier with several Class Conditional Densities such as Gaussian, GMMs (Have to code up EM) 3.2 Bayes’s Classifier with different density estimates (ML, MAP and Parzen Window and nearest neighbor estimates) 5 Also using any one data set do the following:- 5.1 Train a DCGAN for one of the datasets given. Compute FID and plot the generated images in a 10×10 grids. 5.2 Train a VAE on the same dataset and repeat the above experiment. 5.3 Plot t-SNE embeddings for both 2 and 3 dimensions for one of the datasets. 5.4 Compare the above plots with the ones obtained using PCA on the same dataset on 2 and 3 dimens...
Two hidden layers here means (input - hidden1 - hidden2 - output). You must not use flax, optax, or any other library for this task. Use MNIST dataset with 80:20 train:test split. Manually optimize the number of neurons in hidden layers. Use gradient descent from scratch to optimize your network. You should use the Pytree concept of JAX to do this elegantly. Plot loss v/s iterations curve with matplotlib. Evaluate the model on test data with various classification metrics and briefly discuss their implications.
Image processing , DCGAN, Convolution neural networks it's CNN based image recognition project, using DCGAN,,,,As per the document, the task is to generate images using DCGAN and CNN. Model is not mentioned in the document. If you allow me to use pre built model then I can complete the task within 45 hours.,,,, Pls if you can do it start your bid with CNN >>
we will create a model that can detect simple patterns in images using PCA. We will use the MNIST hand-written digit dataset to calculate the PCA eigenvectors and eigenvalues of digit 2 of the MNIST dataset. Then we will use the PCA reconstruction error to detect where the digit appears in a set of larger images, like the ones shown in the Figure below. Figure 1. Example images on which your program will be tested. The PCA reconstruction error is the error (loss) that is introduced when you project a high dimensional vector to a lower-dimensional vector and back-project it to the original high dimension. That error is generally higher when you are projecting a vector that is very dissimilar to the ones that were used to calculate the eigenvectors (e.g. different samples of di...
To use MNIST dataset and train the model using CNN, compare the model with any other model and to show the accuracy. The input will be given by taking the picture of digits written on a paper
you will be given a template that has most of the code already written and you will have to complete a bout 20 - 30 lines in there for the program to run. The program is a simple neural network. two layers. two activation functions one is Relu and one is sigmoid. Data set is MNIST.
...a free country and you may use any other data you wish.) A safe choice would be either the MNIST data or the MNIST-fashion data, which is a drop-in replacement for MNIST (same size data format, same number of classes, same number of training and test examples). A more interesting choice would be CIFAR-10 In setting up the data, you should set up a training set and a test set. The test set should be large enough to give a reasonably accurate assessment of the error-rate (or loss) of your models: preferably at least 10,000 examples. For the learning curve experiment (below), you will need to construct training sets of different sizes, with the largest at least 10 times the smallest. For the MNIST data, for example, your smallest training set might be 500, w...
