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basics, applications, and many more…Deep Learning Garden Liping's machine learning, computer vision, and deep learning home: resources about basics, applications, and many more… Menu Home (Deep) Machine Learning Resources Machine Learning Glossary Deep Learning Glossary Essential Resources for Deep Learning Resources about comparisons of deep learning frameworks TensorFlow resources TensorFlow official links and examples TensorFlow examples from other sources TensorFlow examples (image-based) TensorFlow examples (text-based) TensorFlow setup resources Distributed TensorFlow TensorFlow tagged questions on Stack Overflow Deprecated functions in TensorFlow Some useful TensorFlow related videos on YouTube Keras resources Microsoft Cognitive Toolkit (CNTK) Resources Scikit-learn resources Scikit-learn Resources Scikit-learn official links and examples Scikit-learn examples from other sources Scikit-learn examples (image-based) Scikit-learn examples (text-based) Applications Great blogs OpenAI tools4fun Computer Vision (CV) Resources Computer Vision (CV) resources Resources for Image Processing in OpenCV Computational Geometry in Python Graph Theory Resources GeoAI(Geo) VIZ Resources GeoAI Resources (Geo) VIZ Resources Publications News Jobs About Featured [Paper published] Check out our new paper for image classification with great results using only small sets of training data Do you have great idea(s) using machine learning but stopped by the fact that you do not have enough training (image) data? ? Check out our newly accepted KDD workshop paper for a novel solution. New KDD 2019 MLG (the 15th International Workshop on Mining and Learning with Graphs) Workshop paper for computer vision and image analysis led by Liping has been accepted: Image classification using topological features automatically extracted from graph representation of images . A PDF of the paper can be found at the Workshop website or HERE . BibTeX Entry: @inproceedings{mlg2019_7, title={Image classification using topological features automatically extracted from graph representation of images}, author={Yang, Liping and Oyen, Diane and Wohlberg, Brendt}, booktitle={Proceedings of the 15th International Workshop on Mining and Learning with Graphs (MLG)}, year={2019} } Check out this page for Liping’s more publications. Author LipingY Posted on June 30, 2019 October 22, 2019 Categories Computer vision Leave a comment on [Paper published] Check out our new paper for image classification with great results using only small sets of training data Featured [Paper published] Check out our new (deep) machine learning paper for flood detection New machine/deep learning paper led by Liping: Analysis of remote sensing imagery for disaster assessment using deep learning: a case study of flooding event . A full-text view-only version of the paper can be found via the link: https://rdcu.be/bpUvx . Check out this page for Liping’s more publications. Author LipingY Posted on March 8, 2019 October 22, 2019 Categories Deep learning , Machine Learning_algorithms , Web application dev Tags deep learning , machine learning , tensorflow , visualization , web application Leave a comment on [Paper published] Check out our new (deep) machine learning paper for flood detection Featured [Paper published] Check out our new machine/deep learning paper New machine/deep learning paper led by Liping: Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review https://t.co/kSF3O71tbD Through the synthesis of multiple rapidly developing research areas, this systematic review is relevant to multiple research domains, including but not limited to GIScience, computer science, data science, information science, visual analytics, information visualization, image analysis, and computational linguistics , as well as any domains that need to leverage machine learning and deep learning . Check out this page for Liping’s more publications. Author LipingY Posted on February 22, 2018 October 22, 2019 Categories Active learning , Deep learning , Machine Learning vs Deep Learning Tags active learning , deep learning , machine learning vs deep learning , visualization Leave a comment on [Paper published] Check out our new machine/deep learning paper Featured Install Keras with GPU TensorFlow as backend on Ubuntu 16.04 This post introduces how to install Keras with TensorFlow as backend on Ubuntu Server 16.04 LTS with CUDA 8 and a NVIDIA TITAN X (Pascal) GPU, but it should work for Ubuntu Desktop 16.04 LTS. We gratefully acknowledge the support of NVIDIA Corporation with awarding one Titan X Pascal GPU used for our machine learning and deep learning based research. Keras is a great choice to learn machine learning and deep learning. Keras has easy syntax and can use Google TensorFlow or Microsoft CNTK or Theano as its backend. Keras is simply a wrapper around more complex numerical computation engines such as TensorFlow and Theano . Keras abstracts away much of the complexity of building a deep neural network, leaving us with a very simple, nice, and easy to use interface to rapidly build, test, and deploy deep learning architectures. TensorFlow is extremely flexible , allowing you to deploy network computation to multiple CPUs, GPUs, servers, or even mobile systems without having to change a single line of code. This makes TensorFlow an excellent choice for training distributed deep learning networks in an architecture agnostic way. Now Let’s start on the installation of Keras with TensorFlow as its backend. 1: Setup Python virtual environment Check my post about more details about how to setup python virtual environment and why it is better to install python libraries in Python virtual environment. Install pip and Virtualenv for python and python 3: $ sudo apt-get update $ sudo apt-get install openjdk-8-jdk git python-dev python3-dev python-numpy python3-numpy build-essential python-pip python3-pip python-virtualenv swig python-wheel libcurl3-dev Create a Virtualenv environment in the directory for python and python 3: #for python 2 virtualenv system-site-packages -p python ~/keras-tf-venv # for python 3 virtualenv system-site-packages -p python3 ~/keras-tf-venv3 ( Note: To delete a virtual environment, just delete its folder. For example, In our cases, it would be rm -rf keras-tf-venv or rm -rf keras-tf-venv3 .) 2: UpdateInstall NVIDIA Drivers (skip this if you do not need to TensorFlow GPU version) Check another post I wrote (steps 1-4 in that post) for detailed instructions about how to update and install NVIDIA Drive and CUDA 8.0 and cuDNN for the requirements of GPU TensorFlow installation. Notes: If you have old version of NVIDIA driver installed used the following to remove it first before installation of new driver. Step 1: Remove older version of NVIDIA sudo apt-get purge nvidia* Step 2: Reboot the system test whesther it is removed $ sudo nvidia-smi $ sudo: nvidia-smi: command not found # this means the old driver was uninstalled. ( Note: If you have older version of CUDA and cuDNN installed, check the post for uninstallation. How to uninstall CUDA Toolkit and cuDNN under Linux? (02/16/2017) ( pdf ). Actually to uninstall (older version) of CUDA, it tells you how to uninstall it when you install, see the Install cuda 8.0 below.) ( Note: I tried to install the latest Nvidia drive, latest cuda and latest cudnn (i.e., v6.0), but it did not work for me when I installed TensorFlow. After a few testing, I found when I install Nvidia drive 375.82, cuda_8.0.61_375.26_linux.run, cudnn-8.0-linux-x64-v5.1.tgz. it works.) Install cuda 8.0: Toolkit: Installed in /usr/local/cuda-8.0 Samples: Installed in /home/liping, but missing recommended libraries Please make sure that – PATH includes /usr/local/cuda-8.0/bin – LD_LIBRARY_PATH includes /usr/local/cuda-8.0/lib64, or, add /usr/local/cuda-8.0/lib64 to /etc/ld.so.conf and run ldconfig as root To uninstall the CUDA...