Caffe is a deep learning framework made with expression, speed, and modularity in mind
Expression: models and optimizations are defined as plaintext schemas instead of code.
Speed: for research and industry alike speed is crucial for state-of-the-art models and massive data.
Modularity: new tasks and settings require flexibility and extension.
Openness: scientific and applied progress call for common code, reference models, and reproducibility.
Community: academic research, startup prototypes, and industrial applications all share strength by joint discussion and development in a BSD-2 project.
Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.
Extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models.
Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU*. That’s 1 ms/image for inference and 4 ms/image for learning and more recent library versions and hardware are faster still. We believe that Caffe is among the fastest convent implementations available.
Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example.
Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost.
It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before.
In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers.
Deep learning, a subset of machine learning, utilizes a hierarchical level of artificial neural networks to carry out the process of machine learning. The artificial neural networks are built like the human brain, with neuron nodes connected together like a web. While traditional programs build analysis with data in a linear way, the hierarchical function of deep learning systems enables machines to process data with a non-linear approach.
Linear regression is a linear system and the coefficients can be calculated analytically using linear algebra. ...
Linear regression does provide a useful exercise for learning stochastic gradient descent which is an important algorithm used for minimizing cost functions by machine learning algorithms.
Linear regression is a very simple approach for supervised learning. Though it may seem somewhat dull compared to some of the more modern algorithms, linear regression is still a useful and widely used statistical learning method. Linear regression is used to predict a quantitative response Y from the predictor variable X. Linear Regression is made with an assumption that there’s a linear relationship between X and Y.
Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x).
When there is a single input variable (x), the method is referred to as simple linear regression. When there are multiple input variables, literature from statistics often refers to the method as multiple linear regression.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed
Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
PyShark is a wrapper for the Wireshark CLI interface, tshark, so all of the Wireshark decoders are available to PyShark!
Python wrapper for tshark, allowing python packet parsing using wireshark dissectors.
There are quite a few python packet parsing modules, this one is different because it doesn't actually parse any packets, it simply uses tshark's (wireshark command-line utility) ability to export XMLs to use its parsing.
This package allows parsing from a capture file or a live capture, using all wireshark dissectors you have installed. Tested on windows/linux.
Example Code for Reading a File
import pyshark cap = pyshark.FileCapture('/tmp/mycapture.cap') cap >>> <FileCapture /tmp/mycapture.cap> print cap Packet (Length: 698) Layer ETH: Destination: aa:bb:cc:dd:ee:ff Source: 00:de:ad:be:ef:00 Type: IP (0x0800) Layer IP: Version: 4 Header Length: 20 bytes Differentiated Services Field: 0x00 (DSCP 0x00: Default; ECN: 0x00: Not-ECT (Not ECN-Capable Transport)) Total Length: 684 Identification: 0x254f (9551) Flags: 0x00 Fragment offset: 0 Time to live: 1 Protocol: UDP (17) Header checksum: 0xe148 [correct] Source: 192.168.0.1 Destination: 192.168.0.2
FastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. FastText builds on modern Mac OS and Linux distributions. Since it uses C++11 features, it requires a compiler with good C++11 support.
Steps for Installing
- git clone https://github.com/facebookresearch/fastText.git - cd fastText - make
Text classification is a core problem to many applications, like spam detection, sentiment analysis or smart replies. In this tutorial, we describe how to build a text classifier with the fastText tool.
What is text classification? The goal of text classification is to assign documents (such as emails, posts, text messages, product reviews, etc...) to one or multiple categories. Such categories can be review scores, spam v.s. non-spam, or the language in which the document was typed.
Nowadays, the dominant approach to build such classifiers is machine learning, that is learning classification rules from examples. In order to build such classifiers, we need labeled data, which consists of documents and their corresponding categories (or tags, or labels).