Top 10 Knet Alternatives & Competitors

Top 10 Alternatives to Knet Recently Reviewed By G2 Community

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Keras
By Keras
4.6/5
(64)
Product Description
Keras is a neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
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Reviewers say compared to Knet, Keras is:
More usable
Categories in common with Knet:
Artificial Neural Network

Microsoft Cognitive Toolkit (Formerly CNTK)
By Microsoft
4.2/5
(22)
Product Description
Microsoft Cognitive Toolkit is an open-source, commercial-grade toolkit that empowers user to harness the intelligence within massive datasets through deep learning by providing uncompromised scaling, speed and accuracy with commercial-grade quality and compatibility with the programming languages and algorithms already use.
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Categories in common with Knet:
Artificial Neural Network

AIToolbox
By AIToolbox
4.4/5
(36)
Product Description
AIToolbox is a comprehensive Swift framework designed to facilitate the development and implementation of artificial intelligence algorithms. It offers a suite of AI modules that cater to various machine learning tasks, making it a valuable resource for developers and researchers working within the Swift ecosystem. Key Features and Functionality: - Graphs and Trees: Provides data structures and algorithms for constructing and manipulating graphs and trees, essential for tasks like decision-making processes and hierarchical data representation. - Support Vector Machines (SVMs): Includes tools for implementing SVMs, enabling classification and regression analysis by finding optimal hyperplanes in high-dimensional spaces. - Neural Networks: Offers components to build and train neural networks, facilitating deep learning applications such as image and speech recognition. - Principal Component Analysis (PCA): Contains modules for dimensionality reduction through PCA, aiding in data visualization and noise reduction. - K-Means Clustering: Provides algorithms for partitioning datasets into clusters, useful in pattern recognition and data mining. - Genetic Algorithms: Includes tools for optimization problems using genetic algorithms, simulating natural selection processes to find optimal solutions. Primary Value and User Solutions: AIToolbox addresses the need for a native Swift library that encompasses a broad range of AI functionalities. By integrating multiple machine learning modules into a single framework, it simplifies the development process for Swift developers, eliminating the need to rely on external libraries or languages. This consolidation enhances efficiency, promotes code consistency, and accelerates the deployment of AI-driven applications on Apple platforms.
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Categories in common with Knet:
Artificial Neural Network
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H2O
By H2O.ai
4.5/5
(24)
Product Description
H2O is a tool that makes it possible for anyone to easily apply machine learning and predictive analytics to solve today's most challenging business problems, it combine the power of highly advanced algorithms, the freedom of open source, and the capacity of truly scalable in-memory processing for big data on one or many nodes.
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Reviewers say compared to Knet, H2O is:
More usable
Categories in common with Knet:
Artificial Neural Network

TFLearn
By TFLearn
4/5
(20)
Product Description
TFlearn is a modular and transparent deep learning library built on top of Tensorflow that provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it.
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Reviewers say compared to Knet, TFLearn is:
More usable
Categories in common with Knet:
Artificial Neural Network

PyTorch
By Jetware
4.5/5
(22)
Product Description
PyTorch is an open-source machine learning framework that accelerates the transition from research prototyping to production deployment. Developed by Meta AI and now governed by the PyTorch Foundation under the Linux Foundation, PyTorch is widely used for applications in computer vision, natural language processing, and more. Its dynamic computation graph and intuitive Python interface make it a preferred choice for researchers and developers aiming to build and deploy deep learning models efficiently. Key Features and Functionality: - Dynamic Computation Graph: Allows for flexible and efficient model building, enabling changes to the network architecture during runtime. - Tensors and Autograd: Utilizes tensors as fundamental data structures, similar to NumPy arrays, with support for automatic differentiation to streamline the computation of gradients. - Neural Network API: Provides a modular framework for constructing neural networks with pre-defined layers, activation functions, and loss functions, facilitating the creation of complex models. - Distributed Training: Offers native support for distributed training, optimizing performance across multiple GPUs and nodes, which is essential for scaling large models. - TorchScript: Enables the transition from eager execution to graph execution, allowing models to be serialized and optimized for deployment in production environments. - TorchServe: A tool for deploying PyTorch models at scale, supporting features like multi-model serving, logging, metrics, and RESTful endpoints for application integration. - Mobile Support (Experimental): Extends PyTorch capabilities to mobile platforms, allowing models to be deployed on iOS and Android devices. - Robust Ecosystem: Supported by an active community, PyTorch offers a rich ecosystem of tools and libraries for various domains, including computer vision and reinforcement learning. - ONNX Support: Facilitates exporting models in the Open Neural Network Exchange (ONNX) format for compatibility with other platforms and runtimes. Primary Value and User Solutions: PyTorch's primary value lies in its ability to provide a seamless path from research to production. Its dynamic computation graph and user-friendly interface allow for rapid prototyping and experimentation, enabling researchers to iterate quickly on model designs. For developers, PyTorch's support for distributed training and tools like TorchServe simplify the deployment of models at scale, reducing the time and complexity associated with bringing machine learning models into production. Additionally, the extensive ecosystem and community support ensure that users have access to a wide range of resources and tools to address various machine learning challenges.
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Categories in common with Knet:
Artificial Neural Network

Google Cloud Deep Learning Containers
By Google
4.5/5
(20)
Product Description
Preconfigured and optimized containers for deep learning environments.
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Categories in common with Knet:
Artificial Neural Network

AWS Deep Learning AMIs
By Amazon Web Services (AWS)
4.3/5
(19)
Product Description
The AWS Deep Learning AMIs is designed to equip data scientists, machine learning practitioners, and research scientists with the infrastructure and tools to accelerate work in deep learning, in the cloud, at any scale.
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Reviewers say compared to Knet, AWS Deep Learning AMIs is:
More usable
Categories in common with Knet:
Artificial Neural Network

Neuton AutoML
By Bell Integrator
4.5/5
(17)
Product Description
Neuton, an AutoML platform, allows experienced users and those without any experience in Machine Learning to build compact AI models with just a few clicks and without any coding. Neuton is based on a proprietary neural network framework invented and patented by our team of scientists that is far more effective than any other framework, non-neural algorithm on the market. Its resulting models are self-growing, much more compact, fast and require fewer training samples in comparison to those of other solutions.
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Reviewers say compared to Knet, Neuton AutoML is:
More usable
Categories in common with Knet:
Artificial Neural Network

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