t81_558_deep_learning | Washington University Course T81 | Machine Learning library

 by   jeffheaton Jupyter Notebook Version: 2023-spring License: Non-SPDX

kandi X-RAY | t81_558_deep_learning Summary

kandi X-RAY | t81_558_deep_learning Summary

t81_558_deep_learning is a Jupyter Notebook library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. t81_558_deep_learning has no bugs, it has no vulnerabilities and it has medium support. However t81_558_deep_learning has a Non-SPDX License. You can download it from GitHub.

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN) and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction to mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity of at least one programming language is assumed. This course will be delivered in a hybrid format that includes both classroom and online instruction.
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              t81_558_deep_learning has a medium active ecosystem.
              It has 5430 star(s) with 2972 fork(s). There are 318 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 2 open issues and 116 have been closed. On average issues are closed in 27 days. There are 2 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of t81_558_deep_learning is 2023-spring

            kandi-Quality Quality

              t81_558_deep_learning has 0 bugs and 0 code smells.

            kandi-Security Security

              t81_558_deep_learning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              t81_558_deep_learning code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              t81_558_deep_learning has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

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              t81_558_deep_learning releases are available to install and integrate.
              Installation instructions are not available. Examples and code snippets are available.
              It has 226 lines of code, 7 functions and 6 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

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            t81_558_deep_learning Key Features

            No Key Features are available at this moment for t81_558_deep_learning.

            t81_558_deep_learning Examples and Code Snippets

            No Code Snippets are available at this moment for t81_558_deep_learning.

            Community Discussions

            QUESTION

            tensorflow GPU is not available
            Asked 2021-Jan-16 at 02:29

            I followed through Jeff Heaton's youtube video to install tensorflow with GPU. I have Eva nivida GTX 1660 Ti. After that, it show GPU is not available. any suggestion if something goes wrong? Thanks

            https://www.youtube.com/watch?v=PnK1jO2kXOQ&list=PLrx-l85_irc5IQhZ-yOLq6NjV7GajbT0h&index=8&t=86s https://github.com/jeffheaton/t81_558_deep_learning/blob/master/install/tensorflow-install-jul-2020.ipynb

            ...

            ANSWER

            Answered 2021-Jan-16 at 02:23

            Tensorflow uses CUDA, which the 1660 Ti does not support. Here is a list of all supported gpus: https://developer.nvidia.com/cuda-gpus

            Unfortunately, you can not run tensorflow with gpu-acceleration on that machine. :(

            Source https://stackoverflow.com/questions/65745874

            QUESTION

            OSError: SavedModel file does not exist at: ../dnn/mpg_model.h5/{saved_model.pbtxt|saved_model.pb}
            Asked 2020-May-09 at 16:03

            **

            code editor: vscode

            cmd: anaconda prompt

            I followed the tutorial but why this error? **

            first error was ModuleNotFoundError: No module named 'tensorflow' but i make env and install it second error was ModuleNotFoundError: No module named 'flask' but i make env and install it i fix them and they work on python How can I solve this?

            ...

            ANSWER

            Answered 2020-May-09 at 16:03

            The error occurs because your code is trying to load a model that does not exist. From the Notebook file you linked, you will most likely have to run the following:

            Source https://stackoverflow.com/questions/61699140

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install t81_558_deep_learning

            You can download it from GitHub.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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