Synaptic Description
The basic unit of the neural system is the neuron. They can be connected to other neurons or gate connections between neurons. This allows you to create flexible and complex architectures. Trainers can use any training set and take any network, regardless of its architecture. It also includes tasks to test networks such as learning an XOR or completing a Discrete Sequence Recall task. You can import/export networks to JSON, convert them to workers, or use standalone functions. They can be connected with other networks or gate connections. The Architect has built-in useful architectures like multilayer perceptrons and multilayer long-term memory networks (LSTM), liquid states machines, and Hopfield networks. You can also optimize, extend, export to JSON, convert to Workers or standalone Functions, and even clone networks. A network can be used to project a connection to another or to gate a connection between two networks.
Synaptic Alternatives
Neuri
We conduct cutting-edge research in artificial intelligence and implement it to give financial investors an advantage. Transforming the financial market through groundbreaking neuro-prediction. Our algorithms combine graph-based learning and deep reinforcement learning algorithms to model and predict time series. Neuri aims to generate synthetic data that mimics the global financial markets and test it with complex simulations. Quantum optimization is the future of supercomputing. Our simulations will be able to exceed the limits of classical supercomputing. Financial markets are dynamic and change over time. We develop AI algorithms that learn and adapt continuously to discover the connections between different financial assets, classes, and markets. The application of neuroscience-inspired models, quantum algorithms and machine learning to systematic trading at this point is underexplored.
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SHARK
SHARK is an open-source C++ machine-learning library that is fast, modular, and feature-rich. It offers methods for linear and unlinear optimization, kernel-based algorithms, neural networks, as well as other machine learning techniques. It is a powerful toolbox that can be used in real-world applications and research. Shark relies on Boost, CMake. It is compatible with Windows and Solaris, MacOS X and Linux. Shark is licensed under the permissive GNU Lesser General Public License. Shark offers a great compromise between flexibility and ease of use and computational efficiency. Shark provides many algorithms from different domains of machine learning and computational intelligence that can be combined and extended easily. Shark contains many powerful algorithms that, to our best knowledge, are not available in any other library.
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Microsoft Cognitive Toolkit
The Microsoft Cognitive Toolkit is an open-source toolkit that allows commercial-grade distributed deep-learning. It describes neural networks using a directed graph, which is a series of computational steps. CNTK makes it easy to combine popular models such as feed-forward DNNs (CNNs), convolutional neural network (CNNs), and recurrent neural network (RNNs/LSTMs) with ease. CNTK implements stochastic grade descent (SGD, error-backpropagation) learning with automatic differentiation/parallelization across multiple GPUs or servers. CNTK can be used in your Python, C# or C++ programs or as a standalone machine learning tool via its own model description language (BrainScript). You can also use the CNTK model assessment functionality in your Java programs. CNTK is compatible with 64-bit Linux and 64-bit Windows operating system. You have two options to install CNTK: you can choose pre-compiled binary packages or you can compile the toolkit using the source available in GitHub.
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NeuroIntelligence
NeuroIntelligence, a software application for neural networks, is designed to help experts in data mining, predictive modeling, pattern recognition, and neural network design in solving real-world problems. NeuroIntelligence uses only proven neural net modeling algorithms and techniques. It is easy to use and fast. Visualized architecture search, training and testing of neural networks. Neural network architecture search. Fitness bars. Network training graphs comparison. Training graphs, dataset error and network error, weights distribution, neural network input importance, and errors distribution Testing, actual vs. output graph, scatter plot and response graph, ROC curve and confusion matrix. NeuroIntelligence's interface is optimized to solve data mining and forecasting, classification, and pattern recognition problems. The tool's intuitive GUI and time-saving features make it easy to create a better solution faster.
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Synaptic
Website:
caza.la/synaptic/#/
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