Titlebook: Machine Learning for Computer Scientists and Data Analysts; From an Applied Pers Setareh Rafatirad,Houman Homayoun,Sai Manoj Puduko Textboo

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書目名稱Machine Learning for Computer Scientists and Data Analysts讀者反饋學(xué)科排名

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A Brief Review of Probability Theory and Linear Algebrarete examples. Numerous subjects are explored, including conditional probability, discrete random variables, and continuous random variables. Additionally, the chapter discusses common discrete and continuous probability distributions. The topic of learning matrix decomposition rules applicable to m

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Supervised Learningis the process of acquiring knowledge about annotated data and deriving relationships between the input data and the labels. The simplicity and capacity to develop a better model are two of the key advantages of supervised learning over other types of learning (unsupervised and reinforcement learnin

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Unsupervised Learningls (target), and the machine learning model is supposed to fit the learning curve to the dataset and labels. However, there are numerous situations in which the data may not be labeled. Algorithms are necessary for these circumstances to discover relevant patterns, structures, or groupings within th

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Reinforcement Learninghere exist other scenarios where the data is labeled partially and critical to learning from the experience of the system. For such scenarios, reinforcement learning can be utilized. Reinforcement learning is a kind of machine learning technique that mimics one of the most common learning styles in

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Online Learningt to handle. Online learning approaches strive to update the best predictor for the data in a sequential sequence, as a typical strategy used in areas of machine learning to tackle the computational infeasibility of training throughout the full dataset. In this chapter, we will go through the two ma

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Graph Learningd. Applying typical deep learning techniques (such as convolutional neural networks) to this non-Euclidean structure is not straightforward. As a result, graph neural networks (GNNs) are proposed as a way to combine node attributes with graph topology, thereby establishing themselves as a widely rec

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Adversarial Machine Learningments in the machine learning, the vulnerabilities in those techniques are as well exploited. Adversarial samples are the samples generated by adding crafted perturbations to the normal input samples. An overview of different techniques to generate adversarial samples, defense to make classifiers ro

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SensorNet: An Educational Neural Network Framework for Low-Power Multimodal Data Classificatione-series signals. Time-series signals generated by different sensor modalities with different sampling rates are first converted into images (2-D signals), and then DCNN is utilized to automatically learn shared features in the images and perform the classification. SensorNet: (1) is scalable as it