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.
A New Lightweight, Modular, and Scalable Deep Learning Framework.
Caffe2 aims to provide 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 libraries.
Caffe2 (Convolutional Architecture for Fast Feature Embedding) is an open source, high-performance framework for the development of machine learning models.
Caffe2 is a popular framework due to its speed. The framework can process over 60 million images per day with a single high-performance GPU, like the Nvidia Tesla K40. The framework takes only one millisecond per image for inference and four milliseconds per image for learning.
Caffe2 supports many types of deep learning models and is specialized in image segmentation and image classification. Supported types include convolutional neural networks (CNN), recurrent neural networks (RNN), long short term memory (LSTM) and fully connected neural network designs.
The framework supports Intel CPU acceleration and Nvidia GPGPU along with multi-graphics card implementations. Caffe2 will support AMD OpenCL, FPGAs, AI accelerators and CNN processors.
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.
MLlib (Spark) is Apache Spark’s machine learning library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs.
Regression: generalized linear regression, survival regression,...
Decision trees, random forests, and gradient-boosted trees
Recommendation: alternating least squares (ALS)
Clustering: K-means, Gaussian mixtures (GMMs),...
Topic modeling: latent Dirichlet allocation (LDA)
Frequent itemsets, association rules, and sequential pattern mining
Spark revolves around the concept of a resilient distributed dataset (RDD), which is a fault-tolerant collection of elements that can be operated on in parallel. There are two ways to create RDDs: parallelizing an existing collection in your driver program, or referencing a dataset in an external storage system, such as a shared filesystem, HDFS, HBase, or any data source offering a Hadoop InputFormat.