what are the inquisitorial and adversarial systems?
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An inquisitorial system is a legal system in which the court, or a part of the court, is actively involved in investigating the facts of the case. This is distinct from an adversarial system, in which the role of the court is primarily that of an impartial referee between the prosecution and the defense.
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['Query Decomposition. The first layer decomposes the calculus query into an algebraic query on global relations. ... ', 'Data Localization. The input to the second layer is an algebraic query on global relations. ... ', 'Global Query Optimization. The input to the third layer is an algebraic query on fragments. ... ', 'Distributed Query Execution.']
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The inquisitorial system appears to be more adept at identifying and investigating the relevant facts and ensuring that this is all taken into account when deciding to proceed with a trial. As such, it seems to be a more cost-effective method of conducting a criminal trial.
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['AI and machine learning.', 'Blockchain.', 'Python.', 'Amazon Web Services.', 'Hadoop/Big Data.', 'Mobile technologies (Android or OS)', 'Advanced JavaScript framework (e.g., Angular, React, VueJS)', 'Spring Boot/microservices.']
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['RapidMiner. (435)4.6 out of 5. ... ', 'MATLAB. (541)4.5 out of 5. ... ', 'Tableau Server. (369)4.1 out of 5. ... ', 'RStudio. (486)4.5 out of 5. ... ', 'Google Cloud AI Platform. (82)4.3 out of 5. ... ', 'Azure Machine Learning Studio. (46)4.2 out of 5. ... ', 'Amazon SageMaker. (21)4.2 out of 5. ... ', 'TensorFlow. (38)4.5 out of 5.']
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['Ad Hominem Fallacy. ... ', 'Strawman Argument. ... ', 'Appeal to Ignorance (argumentum ad ignorantiam) ... ', 'False Dilemma/False Dichotomy. ... ', 'Slippery Slope Fallacy. ... ', 'Circular Argument (petitio principii) ... ', 'Hasty Generalization.']
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The classic machine learning procedure follows the scientific paradigm of induction and deduction. In the inductive step we learn the model from raw data (so called training set), and in the deductive step the model is applied to predict the behaviour of new data.
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Generative Adversarial Networks (GANs) for Beginners: Generating Images of Distracted Drivers. Generative adversarial networks (GANs) refers to a set of neural network models typically used to generate stimuli, such as pictures. The use of GANs challenges the dogma that computers are not capable of being creative.
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PyTorch: Custom nn Modules A fully-connected ReLU network with one hidden layer, trained to predict y from x by minimizing squared Euclidean distance. This implementation defines the model as a custom Module subclass.
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['LSTM is a recurrent neural network model that is very efficient at remembering long term dependencies and that is not vulnerable to the vanishing gradient problem. ... ', 'LSTM : Long Short-Term Memory. ( ... ', 'Gradients in LSTMs do vanish, just slower than in vanilla RNNs, enabling them to catch more distant dependencies.']
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['1) Posteriori knowledge :', '2) Priori knowledge :', '3) Dispersed knowledge :', '4) Domain knowledge :', '5) Empirical knowledge :', '6) Encoded knowledge :', '7) Explicit knowledge :', '8) Known unknowns :']
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DEDUCTIVE, INDUCTIVE, AND ABDUCTIVE REASONING. Reasoning is the process of using existing knowledge to draw conclusions, make predictions, or construct explanations. Three methods of reasoning are the deductive, inductive, and abductive approaches. Deductive reasoning: conclusion guaranteed.
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['Artificial Intelligence (AI) ... ', 'Machine Learning. ... ', 'Robotic Process Automation or RPA. ... ', 'Edge Computing. ... ', 'Virtual Reality and Augmented Reality. ... ', 'Cybersecurity. ... ', 'Blockchain. ... ', 'Internet of Things (IoT)']
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Artificial Intelligence (AI) -the broad discipline of creating intelligent machines. Machine Learning (ML) -refers to systems that can learn from experience. Deep Learning (DL) -refers to systems that learn from experience on large data sets.
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ADONIS - Article Delivery Over Network Information Systems.
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['Applicability and Effectiveness,', 'Abstraction and Generality,', 'Simplicity,', 'Logical Derivation, Axiomatic Arrangement,', 'Precision, Correctness, Evolution through Dialectic.']
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However, RNNs suffer from the problem of vanishing gradients, which hampers learning of long data sequences. The gradients carry information used in the RNN parameter update and when the gradient becomes smaller and smaller, the parameter updates become insignificant which means no real learning is done.