Mathworks Matlab R2017b (Mac OSX)

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Mathworks Matlab R2017b (Mac OSX) | 11 GB

MATLAB is a high-performance language for technical computing. It integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation.

Typical uses include:
- Math and computation
- Algorithm development
- Modeling, simulation, and prototyping
- Data analysis, exploration, and visualization
- Scientific and engineering graphics
- Application development, including Graphical User Interface building

MATLAB is an interactive system whose basic data element is an array that does not require dimensioning. This allows you to solve many technical computing problems, especially those with matrix and vector formulations, in a fraction of the time it would take to write a program in a scalar noninteractive language such as C or Fortran.
The name MATLAB stands for matrix laboratory. MATLAB was originally written to provide easy access to matrix software developed by the LINPACK and EISPACK projects, which together represent the state-of-the-art in software for matrix computation.
MATLAB has evolved over a period of years with input from many users. In university environments, it is the standard instructional tool for introductory and advanced courses in mathematics, engineering, and science. In industry, MATLAB is the tool of choice for high-productivity research, development, and analysis.
MATLAB features a family of application-specific solutions called toolboxes. Very important to most users of MATLAB, toolboxes allow you to learn and apply specialized technology. Toolboxes are comprehensive collections of MATLAB functions (M-files) that extend the MATLAB environment to solve particular classes of problems. Areas in which toolboxes are available include signal processing, control systems, neural networks, fuzzy logic, wavelets, simulation, and many others.

Specific deep learning features, products, and capabilities in R2017b include:

- Neural Network Toolbox has added support for complex architectures, including directed acyclic graph (DAG) and long short-term memory (LSTM) networks, and provides access to popular pretrained models such as GoogLeNet.
- The Image Labeler app in Computer Vision System Toolbox now provides a convenient and interactive way to label ground truth data in a sequence of images. In addition to object detection workflows, the toolbox now also supports semantic segmentation using deep learning to classify pixel regions in images and to evaluate and visualize segmentation results.
- A new product, GPU Coder, automatically converts deep learning models to CUDA code for NVIDIA GPUs. Internal benchmarks show the generated code for deep learning inference achieves up to 7x better performance than TensorFlow and 4.5x better performance than Caffe2 for deployed models.
Together with capabilities introduced in R2017a, pretrained models can be used for transfer learning, including convolutional neural networks (CNN) models (AlexNet, VGG-16, and VGG-19), as well as models from Caffe (including Caffe Model Zoo). Models can be developed from scratch, including using CNNs for image classification, object detection, regression, and more.
Additional Updates
In addition to deep learning, R2017b also includes a series of updates in other key areas, including:
Data Analytics with MATLAB
- A new Text Analytics Toolbox product, extensible datastore, more big data plots and algorithms for machine learning, and Microsoft Azure blob storage support
Real-Time Software Modeling with Simulink
- Model scheduling effects and implement pluggable components for software environments
Verification and Validation with Simulink
- New tools for requirements modeling, test coverage analysis, and compliance checking

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