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『github.com』

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  • GitHub - google-deepmind/treescope: An interactive HTML pretty-printer for machine learning research in IPython notebooks.

    3 users

    github.com/google-deepmind

    Treescope is an interactive HTML pretty-printer and N-dimensional array ("tensor") visualizer, designed for machine learning and neural networks research in IPython notebooks. It's a drop-in replacement for the standard IPython/Colab renderer, and adds support for: Expanding and collapsing subtrees of rendered objects, to let you focus on the parts of your model that you care about, Automatically

    • テクノロジー
    • 2024/07/27 11:06
    • あとで読む
    • GitHub - google-deepmind/mctx: Monte Carlo tree search in JAX

      3 users

      github.com/google-deepmind

      Learning and search have been important topics since the early days of AI research. In the words of Rich Sutton: One thing that should be learned [...] is the great power of general purpose methods, of methods that continue to scale with increased computation even as the available computation becomes very great. The two methods that seem to scale arbitrarily in this way are search and learning. Re

      • テクノロジー
      • 2023/02/16 17:50
      • search
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      • GitHub - google-deepmind/educational

        8 users

        github.com/google-deepmind

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        • テクノロジー
        • 2022/09/25 10:40
        • 機械学習
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        • GitHub - google-deepmind/mujoco_menagerie: A collection of high-quality models for the MuJoCo physics engine, curated by Google DeepMind.

          3 users

          github.com/google-deepmind

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          • テクノロジー
          • 2022/09/12 10:51
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          • GitHub - google-deepmind/mujoco: Multi-Joint dynamics with Contact. A general purpose physics simulator.

            5 users

            github.com/google-deepmind

            MuJoCo stands for Multi-Joint dynamics with Contact. It is a general purpose physics engine that aims to facilitate research and development in robotics, biomechanics, graphics and animation, machine learning, and other areas which demand fast and accurate simulation of articulated structures interacting with their environment. This repository is maintained by Google DeepMind. MuJoCo has a C API a

            • テクノロジー
            • 2021/10/20 02:48
            • robotics
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            • GitHub - google-deepmind/xmanager: A platform for managing machine learning experiments

              6 users

              github.com/google-deepmind

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              • テクノロジー
              • 2021/10/18 19:39
              • google
              • GitHub - google-deepmind/alphafold: Open source code for AlphaFold 2.

                13 users

                github.com/google-deepmind

                This package provides an implementation of the inference pipeline of AlphaFold v2. For simplicity, we refer to this model as AlphaFold throughout the rest of this document. We also provide: An implementation of AlphaFold-Multimer. This represents a work in progress and AlphaFold-Multimer isn't expected to be as stable as our monomer AlphaFold system. Read the guide for how to upgrade and update co

                • テクノロジー
                • 2021/07/16 09:36
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                • GitHub - google-deepmind/tree: tree is a library for working with nested data structures

                  20 users

                  github.com/google-deepmind

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                  • テクノロジー
                  • 2021/01/24 14:41
                  • Python
                  • OSS
                  • data
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                  • GitHub - google-deepmind/open_spiel: OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.

                    4 users

                    github.com/google-deepmind

                    OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and sequential, strictly turn-taking and simultaneous-move, perfect and imperfect information games, as well as traditional multiagent environments such as (partia

                    • テクノロジー
                    • 2019/08/28 13:24
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                    • GitHub - deepmind/graph_nets: Build Graph Nets in Tensorflow

                      21 users

                      github.com/google-deepmind

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                      • テクノロジー
                      • 2018/10/19 07:15
                      • DeepMind
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                      • GitHub - deepmind/trfl: TensorFlow Reinforcement Learning

                        6 users

                        github.com/google-deepmind

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                        • テクノロジー
                        • 2018/10/18 03:51
                        • あとで読む
                        • GitHub - google-deepmind/kinetics-i3d: Convolutional neural network model for video classification trained on the Kinetics dataset.

                          3 users

                          github.com/google-deepmind

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                          • テクノロジー
                          • 2018/07/17 13:31
                          • あとで読む
                          • deepmind/dnc: A TensorFlow implementation of the Differentiable Neural Computer.

                            8 users

                            github.com/google-deepmind

                            The Differentiable Neural Computer is a recurrent neural network. At each timestep, it has state consisting of the current memory contents (and auxiliary information such as memory usage), and maps input at time t to output at time t. It is implemented as a collection of RNNCore modules, which allow plugging together the different modules to experiment with variations on the architecture. The acce

                            • テクノロジー
                            • 2017/04/20 02:14
                            • GitHub - google-deepmind/sonnet: TensorFlow-based neural network library

                              28 users

                              github.com/google-deepmind

                              Sonnet has been designed and built by researchers at DeepMind. It can be used to construct neural networks for many different purposes (un/supervised learning, reinforcement learning, ...). We find it is a successful abstraction for our organization, you might too! More specifically, Sonnet provides a simple but powerful programming model centered around a single concept: snt.Module. Modules can h

                              • テクノロジー
                              • 2017/04/08 01:33
                              • TensorFlow
                              • deep learning
                              • DeepLearning
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                              • あとで読む
                              • GitHub - google-deepmind/learning-to-learn: Learning to Learn in TensorFlow

                                6 users

                                github.com/google-deepmind

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                                • テクノロジー
                                • 2016/12/07 21:19
                                • GitHub
                                • あとで読む
                                • GitHub - google-deepmind/lab: A customisable 3D platform for agent-based AI research

                                  9 users

                                  github.com/google-deepmind

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                                  • テクノロジー
                                  • 2016/12/07 02:12

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