Artificial Intelligence has now more programmers acquiring this science. It is increasing as there is steady growth of AI tools and the needs are increasing for AI technologies. Here is a guide to start learning about this growing powerful field.
List of AI tools and Frameworks
Caffe is suitable for industrial implementations and scientific projects as it handles more than 60M photos with one NVIDIA GPU. The AI framework established as a co-evolutionary neural network (CNN) presents a solid architecture to optimize hard coding. It is to follow defined configuration systems. The AI framework shows its responsiveness to Python, C++, MALAB interfaces, and CUDA to command line. It is also useful when you want to change from GPU and CPU.
TensorFlow is a popular framework and is open source. It is a machine learning library. It has the backing from Google and has client-base such as eBay, Airbnb, Coca-cola, and Dropbox. The AI department of Google is now one of the favorite artificial intelligence tools. This tool of Google AI is the preferable choice of tech giants such SAP, AMD, Vvidia, and Intel. It is suitable for high volumes having complex numerical computations using machine learning and data-flow graphs. This framework allows computation on GPU or CPU as it is compatible to a mobile device, server, or a desktop.
Torch is for numerical and scientific processing. It is a computation system that produces algorithm with versatility, pace, and usability. The consumers of Torch have libraries easy-to-use, thereby allowing artificial logic modular application. The Torch first puts GPUs and has the C/CUDA fundamental integration. The performance shows good increase and the deep learning is on demand as it has algorithms in vast number. It improves procedures like indexing and cutting, besides including neural networks and linear algebra protocols.
4. Microsoft CNTK
Microsoft CNTK or Cognitive Toolkit is a deep learning toolkit. It describes neural networks using a directed graph as computational steps. It is an open-source program designed to support algorithms and commercial-grade robust datasets. It enhances model descriptions and computation networks. It goes well with C++, Python, Java, C#, or any .NET language. It is useful in AI application and there is no compromise on the quality accuracy or speed. It also serves Bing, Cortana, and Skype. The biggest advantage of this list of AI Tools and Frameworks is that it allows distributed training and is flexible.
One of the methods accessible for AI is Scikit-learn and it is commercially available as AI framework. It is a Python program allowing machine learning of unsupervised and supervised programs. The tools are easy to access and Sci-kit programming features various capabilities such as unmonitored, controlled, and cross-validation learning of algorithms. It is a machine learning program in Python. This open-source tool is multipurpose AI tool for development. It supports clustering, regression, and classification of algorithms.
Theano offers a tough competition to TensorFlow. Theano is an artificial intelligence tool that allows optimizing, defining, and assessing mathematical expressions. It involves multi-dimensional arrays and evaluates expressions matrix-valued. This AI library carries out GPU transparently to perform data-intensive computations and the operations results prove it is highly efficient. Theano can be useful for intensive computation operations. It helps resolving complicated mathematical expressions requiring fast and repeated evaluation. The advantage of this artificial intelligence tool is that it is mainly in use for GPU and CPU, and is suitable for numerical tasks. It offers extensive learning framework that it is useful to hundreds of organizations as it offers AI applications that are ready-to-use.