NeuroIntelligence
NeuroIntelligence is a neural networks software application designed to assist neural network, data mining, pattern recognition, and predictive modeling experts in solving real-world problems. NeuroIntelligence features only proven neural network modeling algorithms and neural net techniques; software is fast and easy-to-use. Visualized architecture search, neural network training and testing. Neural network architecture search, fitness bars, network training graphs comparison. Training graphs, dataset error, network error, weights and errors distribution, neural network input importance. Testing, actual vs. output graph, scatter plot, response graph, ROC curve, confusion matrix. The interface of NeuroIntelligence is optimized to solve data mining, forecasting, classification and pattern recognition problems. You can create a better solution much faster using the tool's easy-to-use GUI and unique time-saving capabilities.
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NVIDIA DIGITS
The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning into the hands of engineers and data scientists. DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real-time with advanced visualizations, and selecting the best performing model from the results browser for deployment. DIGITS is completely interactive so that data scientists can focus on designing and training networks rather than programming and debugging. Interactively train models using TensorFlow and visualize model architecture using TensorBoard. Integrate custom plug-ins for importing special data formats such as DICOM used in medical imaging.
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Chainer
A powerful, flexible, and intuitive framework for neural networks. Chainer supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort. Chainer supports various network architectures including feed-forward nets, convnets, recurrent nets and recursive nets. It also supports per-batch architectures. Forward computation can include any control flow statements of Python without lacking the ability of backpropagation. It makes code intuitive and easy to debug. Comes with ChainerRLA, a library that implements various state-of-the-art deep reinforcement algorithms. Also, with ChainerCVA, a collection of tools to train and run neural networks for computer vision tasks. Chainer supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort.
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Neuralhub
Neuralhub is a system that makes working with neural networks easier, helping AI enthusiasts, researchers, and engineers to create, experiment, and innovate in the AI space. Our mission extends beyond providing tools; we're also creating a community, a place to share and work together. We aim to simplify the way we do deep learning today by bringing all the tools, research, and models into a single collaborative space, making AI research, learning, and development more accessible. Build a neural network from scratch or use our library of common network components, layers, architectures, novel research, and pre-trained models to experiment and build something of your own. Construct your neural network with one click. Visually see and interact with every component in the network. Easily tune hyperparameters such as epochs, features, labels and much more.
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