Convolutional neural networkDeep learningNatural language processingOptimization algorithmsSentiment analysisText classificationLately, deep learning has improved the algorithms and the architectures of several natural language processing (NLP) tasks. In spite of that, the performance of any deep learning model...
Neural networks in machine learningrefer to a set of algorithms designed to help machines recognize patterns without being explicitly programmed. They consist of a group of interconnected nodes. These nodes represent the neurons of the biological brain. The basic neural network consists of: The input...
Neural Network Compression Framework (NNCF) provides a suite of post-training and training-time algorithms for optimizing inference of neural networks inOpenVINO™with a minimal accuracy drop. NNCF is designed to work with models fromPyTorch,TorchFX,TensorFlow,ONNXandOpenVINO™. ...
Further, the assumptions people make when training algorithms cause neural networks to amplify cultural biases.Biased data sets are an ongoing challengein training systems that find answers on their own through pattern recognition in data. If the data feeding the algorithm isn't neutral -- and almo...
pythonmachine-learningdeep-learningmachine-learning-algorithmsneural-networks UpdatedMar 30, 2023 Python Tutorials, assignments, and competitions for MIT Deep Learning related courses. data-sciencemachine-learningmitdeep-learningtensorflowdeep-reinforcement-learningartificial-intelligenceneural-networkssegmentationtensorf...
Traditional text classification works mainly focus on three topics: feature engineering, feature selection and using different types of machine learning algorithms. For feature engineering, the most widely used feature is the bag-of-words feature. In addition, some more complex features have been desig...
Recurrent neural networks use forward propagation and backpropagation through time (BPTT) algorithms to determine the gradients (or derivatives), which is slightly different from traditional backpropagation as it is specific to sequence data. The principles of BPTT are the same as traditionalbackpropagat...
simpler tasks or problems where data is limited, traditional algorithms might be more suitable. For instance, if you're sorting a small list of numbers or searching for a specific item in a short list, a basic algorithm would be more efficient and faster than setting up a neural network. ...
Logical Explanations for Deep Relational Machines Using Relevance Information JMLR Paper This work provides a methodology to generate symbolic explanations for predictions made by a deep neural network constructed from relational data, called DRMs. It investigates the use of a Bayes-like approach to iden...
The “deep” in deep learning refers to the multiple layers of artificial neurons in a network. Compared with neural nets, which are better at solving smaller problems, deep learning algorithms are capable of more complex processing because of their interconnected layers of nodes. While they are ...