Python is a high-level general purpose programming language, and its open source machine learning library is called Pytorch. It is currently being utilized by two of the tech sector’s most valuable companies, Facebook and Uber, who utilize it for different reasons. It is considered one of the most important tools in artificial intelligence today. It provides two main features, which include tensor computation, in addition to deep neural networks. The language is also known for utilizing a technique called automatic differentiation, which allows for rapid machine learning.

One of the most important aspects for automation and the internet of things is the idea of natural language processing, which allows human beings to interact with devices through voice. Human beings understand that the more that computers understand human languages, the more that automation can occur, not to mention real-world applications, such as language translation and transcription.

Pytorch is one of the most important libraries related to machine learning and deep learning, that is already being used by multiple Fortune 500 companies. Its relevancy will only increase the more that we move towards using artificial intelligence in everyday technology, and Pytorch can be a tool that can optimize countless companies exponentially.

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    I need work support my work is related to automation testing using linux, jenkins, integration testing framework level

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    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.

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    I can ONNX export the pytorch model created with the notebook file at But the ONNX model created here only contains feature maps and what I want is to create an interpreter with dynamic input and output working with OpenCV in C++. I prefer it to work with opencv dnn module. But onnxruntime can also use. Please take a look at the files I uploaded before applying. I can give 2 days as the completion time. Anyone who can do this project should have it done in 1-2 days at the most.

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