Machine Learning Jobs

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Fixed-Price - Expert ($$$) - Est. Budget: $1,000 - Posted
We have: - millions of user profiles from social media, each with 10+ variables including integers and free form text. - 15 categories to put these profiles into. - about 200k profiles already categorized. We would like to create a TensorFlow model for this multiple classification process. The solution needs to be hosted, to be queried via API calls, either on AWS or Google Cloud. Budget negotiable.
Skills: Machine learning TensorFlow
Fixed-Price - Intermediate ($$) - Est. Budget: $250 - Posted
Hi ! I got few simple tool written in python ! Problem in them is that those tools are very slow ! I need decent performance improvemnt for them ! Tool 1:!M00BVCxS!n4UbvDvv-z4PMTE9iLcdrAWBH3F_tVgB5KGL_AmufcQ Consits of few modules: - - add multithreading - improve algorithm for better performance if possible - maybe using indexed database instesd of file as data source would help? - - add multithreading if possible - make as fast algorithm as possible - should work with files like 50GB but not eat more than 75% of computer RAM - - already multothreaded ! but still very slow !! need decent improvemnt because LSA* scripts are most important to me Tool2:!8wEwAZQS!s8EAQK27Lh1IiA5XMbjGv1oISjbmSyOXKwIK5Phbatw removes duplicates from parallel sets of texts. Now it works but problem is that I want to process 300GB data and the tool tries to load all into memory that needs to be solved. Also introducing multithreading is needed. Other algorithmical improvements are not needed but well seen if possible. Tool3:!E0tkUKDY!4CxlRzz3omo2ux3CpUjtsqls0wWw_QLkPbEGgHdG4uI Works good, but too slow. I would need to add multhreading (but the tools should not consume more that 75% of PC RAM). Other algorithmical improvements are in my opinion requred if possible.
Skills: Machine learning Artificial Neural Networks CUDA Deep Neural Networks
Fixed-Price - Expert ($$$) - Est. Budget: $1,000 - Posted
We are a company that has built a tool that connects multiple accounting systems to allow the interchange of invoices between them. We want to run a test with machine learning to see whether we can extract specific data from pdf invoices. If successful, we'll built it out to about 15 pieces of data. In the test, we'll focus on - ABN - Invoice Number - BSB - Bank Account Number - Invoice Total Most data can be found by looking for a range of keywords that are close-by (either before and above the data). To join our team, please answer the questions. As a next step, we'll invite suitable candidates to a Skype interview with a view for an immediate start.
Skills: Machine learning TensorFlow
Hourly - Intermediate ($$) - Est. Time: Less than 1 week, 10-30 hrs/week - Posted
Looking for a python programmer familiar with the sci-kit learn package to work on a small regresion analysis. To begin with, this will be a short python program, but there may be future work available if the results are good. Experience with stock trading or other time based datasets will be required. Further information and dataset will be made available to the selected candidate.
Skills: Machine learning Data Science Python Python Numpy
Hourly - Expert ($$$) - Est. Time: 1 to 3 months, Less than 10 hrs/week - Posted
I need help in the designing and improving the existing system which is heavily based on NLP. I am looking for some one who have PhD in this area and suggest me good approach and clear my doubts in the area of NLP and AI. I can take care of coding myself. Web scrapping, resume parsing/matching and chatbot experience is a plus.
Skills: Machine learning Artificial Intelligence Deep Learning Python
Fixed-Price - Expert ($$$) - Est. Budget: $750 - Posted
The objective of the PoC is to come up with a Java Program / Class that will combine a few available libraries for comparing images for similarity even if the image is resized or compressed. image = new, image2) image.getSimilarityRating() image.getSameSize() image.getSameCompression() For similarity use size, compression, and AI methods to determine if the images are the same. There are few examples on stack overflow. Per
Skills: Machine learning Java
Hourly - Intermediate ($$) - Est. Time: Less than 1 month, 10-30 hrs/week - Posted
Hello, I am looking for someone out there to help with a natural language processing / machine learning problem. I have a series of similar but inconsistent text files that have numbered hierarchical paragraphs of unstructured text with defined terms scattered throughout the blocks of unstructured text. The attached document has more information as well as a sample of the format. The exact structure therein cannot be counted on to appear exactly as shown, but there will be some sort of hierarchy following this format. There may or may not be indenting. You can’t do the parsing based on formatting because it will not be consistent across files. Somehow, however, you will have separate, hierarchical paragraphs with some sort of numbering and / or lettering scheme similar to the one shown below. Ideally, I'd like to parse this document into an xml file. I want to preserve the hierarchy of the document in the xml file. Within the xml file, I’d like to achieve two things in particular. First, all defined terms should be tagged with the xml tags for <definition></definition>. Second, if there are internal references to other parts of the file, I’d like those references to be tagged and refer to the corresponding part of the xml document. For example, see some examples in 2(a)(j) of the attachment. As you can see, you might see a reference set off by Section #. You also might see a reference to another section that is a child of the same parent section – e.g. Subsection b of this subsection. I’d prefer a solution in python or Java. You are free to use whatever open source software or libraries you choose. A sample format with more information is attached as a word doc
Skills: Machine learning Data Entry Data mining Java
Fixed-Price - Expert ($$$) - Est. Budget: $700 - Posted
Need a machine learning expert to develop a l matching tool that would take unstructured (text based) eligibility criteria, convert into structured data and use classification, build semantic clusters, ontology map etc. The entire data is avaiable as postgres DB dump or Basically this tool will help patients and their families to find clinical trials for their chronic condition (start with cancer). Its a novel cause that is a signficant problem.. I have a couple of research papers that outlines most of the approach. Just needs to be implemented. Will be exposed as APIs. So that other frontend webapps / native apps can use it.
Skills: Machine learning API Development Data Science Python