Help Center> >Glossary


    • C

      See Convolutional Neural Networks

      Convolutional Neural Networks

      In machine learning, a convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural networks that has successfully been applied to analyzing visual imagery.


      • D
        Deep Learning

        Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised.

        Deep Learning Service

        Deep Learning Service (DLS) allows you to implement model development, debugging, training, and inference. It is applicable to image and video processing, natural language processing, and recommendation.

        Deep Neural Networks

        A deep neural network (DNN) is an artificial neural network (ANN) with multiple hidden layers between the input and output layers. DNNs can model complex non-linear relationships. DNN architectures generate compositional models where the object is expressed as a layered composition of primitives.

        Development environment

        A development environment is a Jupyter Notebook service, which facilitates development, debugging, and saving of service code. (It is recommended that Xlearnt be used for high-performance distributed training.)


        See Deep Learning


        See Deep Learning Service


        See Deep Neural Networks


        • I
          Inference job

          An inference job hosts a model inference service.


          • K

            See kubernetes


            Kubernetes (commonly referred to as "K8s") is an open-source system for automating deployment, scaling and management of containerized applications that was originally designed by Google and donated to the Cloud Native Computing Foundation.


            • M
              Machine Learning

              Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed.


              See Machine Learning


              • N
                Neural Networks

                Neural network, is a computing system inspired by the biological neural networks that constitute animal brains.


                See Neural Networks


                • P
                  Preset model

                  A preset model is a pre-trained model that can be used to re-train the new data set without compiling the model training code.


                  • R
                    Recurrent Neural Networks

                    A recurrent neural network (RNN) is a class of artificial neural network where connections between units form a directed cycle. This allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. This makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.


                    See Recurrent Neural Networks


                    • S
                      Startup file

                      A startup file is a Python startup script in the directory where model training code resides.


                      • T

                        TensorFlow is an open-source software library for dataflow programming across a range of tasks. It is a symbolic math library, and also used for machine learning applications such as neural networks.


                        See TensorFlow

                        Training job

                        A training job is a model training process.