Search Results for "python-dpkt" - Page 21
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Showing 571 open source projects for "python-dpkt"
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AI-Blocks
A powerful and intuitive WYSIWYG to create Machine Learning models
...The concept of AI-Blocs is to have a simple scene with draggable objects that have scripts attached to them. The model can be run directly on the editor or be exported to a standalone script that runs on Tensorflow. Variables are parsed from python scripts and can be edited from the AI-Blocs properties panel. To run your model simply press the "Play" button and let the magic happen! The project requires Python and Tensorflow to run projects. You can still create and edit projects without these dependencies. To run AI-Blocs, download the project archive and launch AI-Blocs.exe. -
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Tangent
Source-to-source debuggable derivatives in pure Python
...Tangent works on a large and growing subset of Python, provides extra autodiff features other Python ML libraries don't have, has reasonable performance, and is compatible with TensorFlow and NumPy. -
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Deepo
Set up deep learning environment in a single command line
Deepo is a series of Docker images that allows you to quickly set up your deep learning research environment, supports almost all commonly used deep learning frameworks, supports GPU acceleration (CUDA and cuDNN included), also works in CPU-only mode, and works on Linux (CPU version/GPU version), Windows (CPU version) and OS X (CPU version). Their Dockerfile generator that allows you to customize your own environment with Lego-like modules, and automatically resolves the dependencies for... -
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Five video classification methods
Code that accompanies my blog post outlining five video classification
Classifying video presents unique challenges for machine learning models. As I’ve covered in my previous posts, video has the added (and interesting) property of temporal features in addition to the spatial features present in 2D images. While this additional information provides us more to work with, it also requires different network architectures and, often, adds larger memory and computational demands.We won’t use any optical flow images. This reduces model complexity, training time, and... -
Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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NNVM
Open deep learning compiler stack for cpu, gpu
...Automatically generate and optimize tensor operators on more backends. Need support for block sparsity, quantization (1,2,4,8 bit integers, posit), random forests/classical ML, memory planning, MISRA-C compatibility, Python prototyping or all of the above? NNVM flexible design enables all of these things and more. -
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AI learning
AiLearning, data analysis plus machine learning practice
We actively respond to the Research Open Source Initiative (DOCX) . Open source today is not just open source, but datasets, models, tutorials, and experimental records. We are also exploring other categories of open source solutions and protocols. I hope you will understand this initiative, combine this initiative with your own interests, and do what you can. Everyone's tiny contributions, together, are the entire open source ecosystem. We are iBooker, a large open-source community,... -
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auto_ml
Automated machine learning for analytics & production
auto_ml is designed for production. Here's an example that includes serializing and loading the trained model, then getting predictions on single dictionaries, roughly the process you'd likely follow to deploy the trained model. Before you go any further, try running the code. Load up some data (either a DataFrame, or a list of dictionaries, where each dictionary is a row of data). Make a column_descriptions dictionary that tells us which attribute name in each row represents the value we’re... -
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Deep Learning with Keras and Tensorflow
Introduction to Deep Neural Networks with Keras and Tensorflow
Introduction to Deep Neural Networks with Keras and Tensorflow. To date tensorflow comes in two different packages, namely tensorflow and tensorflow-gpu, whether you want to install the framework with CPU-only or GPU support, respectively. NVIDIA Drivers and CuDNN must be installed and configured before hand. Please refer to the official Tensorflow documentation for further details. Since version 0.9 Theano introduced the libgpuarray in the stable release (it was previously only available in... -
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Chronological Cohesive Units
The experimental source code for the paper
The experimental source code for the paper, "A Novel Recommendation Approach Based on Chronological Cohesive Units in Content Consuming" -
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Lip Reading
Cross Audio-Visual Recognition using 3D Architectures
The input pipeline must be prepared by the users. This code is aimed to provide the implementation for Coupled 3D Convolutional Neural Networks for audio-visual matching. Lip-reading can be a specific application for this work. Audio-visual recognition (AVR) has been considered as a solution for speech recognition tasks when the audio is corrupted, as well as a visual recognition method used for speaker verification in multi-speaker scenarios. The approach of AVR systems is to leverage the... -
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Machine Learning for OpenCV
M. Beyeler (2017). Machine Learning for OpenCV
M. Beyeler (2017). Machine Learning for OpenCV: Intelligent image processing with Python. Packt Publishing Ltd., ISBN 978-178398028-4. -
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Caffe2
Caffe2 is a lightweight, modular, and scalable deep learning framework
Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind. Caffe2 is a deep learning framework that provides an easy and straightforward way for you to experiment with deep learning and leverage community contributions of new models and algorithms. You can bring your creations to scale using the power of GPUs in the cloud or to the masses on mobile with Caffe2’s cross-platform... -
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Training Image Operators from Samples
Tools to train Image Operators automatically from a set of samples.
TRIOS - Training Image Operators from Samples is a set of tools to bring Image Processing closer to scientists in general. It is capable of estimating an operator between two images using only pairs of samples that contain an input image and the desired output. The operator is saved to a file and can be applied to any image. -
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TensorFlow on Raspberry Pi
TensorFlow for Raspberry Pi
TensorFlow on Raspberry Pi. -
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Scattertext 0.2.1
Beautiful visualizations of how language differs among document types
A tool for finding distinguishing terms in corpora and displaying them in an interactive HTML scatter plot. Points corresponding to terms are selectively labeled so that they don't overlap with other labels or points. -
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PyDaMelo
Python-compatible Data mining elementary objects
An attempt at offering machine learning and data mining algorithms at the finest grain we are able to, easy to combine together through Python scripting to glue together the Lego-like bricks. -
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TEES
Turku Event Extraction System
Turku Event Extraction System (TEES) is a free and open source natural language processing system developed for the extraction of events and relations from biomedical text. It is written mostly in Python, and should work in generic Unix/Linux environments. Currently, the TEES source code repository still remains on GitHub at http://jbjorne.github.com/TEES/ where there is also a wiki with more information. -
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Python Machine Learning book
The book code repository and info resource
What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning. From theory to the actual code that you can directly put into action! This is not yet just another "this is how scikit-learn works" book. I aim to explain all the underlying concepts, tell you everything you need to know in terms of best practices and caveats, and we will put those concepts into action mainly using NumPy, scikit-learn, and Theano. This is not... -
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Spark Python Notebooks
Apache Spark & Python (pySpark) tutorials for Big Data Analysis
Spark Python Notebooks is a curated collection of example Jupyter notebooks designed to help developers and data engineers learn Apache Spark using Python in an interactive environment. Rather than only providing static code files, this project uses notebooks to teach practical data processing workflows, exposing users to real Spark programming patterns like working with RDDs, DataFrames, and distributed computations. -
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AWS IoT Arduino Yún SDK
SDK for connecting to AWS IoT from an Arduino Yún
...The AWS-IoT-Arduino-Yún-SDK consists of two parts, which take use of the resources of the two chips on Arduino Yún, one for native Arduino IDE API access and the other for functionality and connections to the AWS IoT built on top of AWS IoT Device SDK for Python. The AWS-IoT-Arduino-Yún-SDK provides APIs to let users publish messages to AWS IoT and subscribe to MQTT topics to receive messages transmitted by other devices or coming from the broker. This allows to interact with the standard MQTT PubSub functionality of AWS IoT. -
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Convolution arithmetic
A technical report on convolution arithmetic in deep learning
A technical report on convolution arithmetic in the context of deep learning. The code and the images of this tutorial are free to use as regulated by the licence and subject to proper attribution. The animations will be output to the gif directory. Individual animation steps will be output in PDF format to the pdf directory and in PNG format to the png directory. We introduce a guide to help deep learning practitioners understand and manipulate convolutional neural network architectures.... -
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e-Metis - ML
Modul za napovedovanje učnih težav.
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openModeller is a complete C++ framework for species potential distribution modelling. The project also includes a graphical user interface, a web service interface and an API for Python.
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Machine Learning framework in Python
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ExSTraCS
Extended Supervised Tracking and Classifying System
...It can flexibly handle (1) discrete or continuous attributes, (2) missing data, (3) balanced or imbalanced datasets, and (4) binary or many classes. A complete users guide for ExSTraCS is included. Coded in Python 2.7.
