Mac Software. Adium 1.5.10.4. Adobe Acrobat 10.1.14. MarvinBeans 17.20. Mathematica 11.2. MATLAB R2017b. Mindjet 10.3.635. MPEG StreamClip 1.9.2. How to download MATLAB_R2017b for mac? Learn more about download matlab for mac.
Matlab r2017b for mac免费版是一款著名商业数学软件，软件非常的经典而且好用，到现在已经推出众多版本；matlab2017b全新版本为满足用户的需要加入更多实用性功能，同时对已有功能进行优化和升级，本站提供matlab2017b mac下载和pj文件。 matlab2017b mac免费版简介： 是面向工程师和科学家的最简单和最具生产力的软件，无论您要分析数据、开发算法还是创建模型，MATLAB都提供了鼓励探索和发现的环境，它将高级语言与针对迭代式工程和科学工作流进行调整的桌面环境相结合。广泛地应用于工程计算、控制设计、信号处理与通讯、图像处理、信号检测、金融建模设计与分析等领域。 全球数以百万计的工程师和科学家使用 MATLAB® 来分析和设计可改变世界的系统和产品。MATLAB 广泛应用于汽车主动安全系统、行星际宇宙飞船、健康监控设备、智能电网和 LTE 蜂窝网络。它用于机器学习、信号处理、图像处理、计算机视觉、通讯、计算金融学、控制设计、机器人学等等。 matlab2017b mac新增功能： Matlab线性代数和数值计算方面优势显著， 向量化运算往往比同类软件更快， 拥有超多工具箱，仿真，图像处理，信号处理，金融，统计，优化. 程序语言比较易学，编辑和调试环境不错 方便构建GUI 不是原生支持符号计算（符号计算远不止是推导公式），新版的mupad内核还不错，但是和Mathematica、Maple比有明显差距，不论是深度、广度和速度递归特别慢，比Mathematica和Maple以及常见的脚本语言都慢在一些数学领域相对薄弱，如数论，图论，离散数学等.
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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.
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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.