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 to fine-tune two different pre-trained wav2vec 2.0 models. The model should be fine-tuned for both Hungarian and Turkish languages (separate models). The main dataset will be CommonVoice and models should be integrated with the n-gram language model. And well commented ( short documentation ) is needed. Deadline: 2-3 days.

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    This project is a continuation of the kaggle completion whose description can be found here: (). There will be an attached GitHub repo that already uses pertained models for the competition task of predicting 3 possible trajectories for self driving cars. For this project I am looking for someone to work with the partial training set as provided and create a ensemble model combining the models Xception41 and Xception65 with ResNet using k means methods returning 3 hypothesized trajectory predictions for the car agents. GithubRepo:

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    Working through a Jupyter Notebook and implement a PixelCNN. A already have some approaches for the first tasks

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    Preparing datasets for training ● Find public data and/or restructure data suitable for the project ● Responsible for design and development of different types of computer vision models that fits the requirement. ● Understanding of different data structures and ability to convert from structure to another ● Understanding of different Deep Learning Libraries (eg. Tensorflow, PyTorch, openvino etc) ● Create functions for training and inference ● Test the inference time on different types of servers/GPUS/CPUs/Edge devices ● Build ML modules, along with accuracy, must also deliver the production workload expectations - work with real world data, suitable inference time, compatibility with tech-stack, work on CPUs where applicable ● Understand different types of ML problems - classification, re...

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    I have a project wherein I want to calculate accuracy of Pytorch Model. I am using kaggle notebook. Actually I wanted to calculate the accuracy of the model and if required I wanted to improve the model for better accuracy, also want to have a code walk thru. URL -

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    I am working on combining a U-Net (model) with a Vision Transformer (model) to achieve the task of removing weather-based noise from input images (rain, snow, fog). My method trains a little bit and stop after few initial epochs then reports error in computing the first PSNR and SSIM metrics. I can't seem to figure out the problem. At this point I need help to make the model train successfully and achieve good results. Freelancer with good knowledge of Python3, PyTorch, and deep learning will be very useful.

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