That Was Then This Is Now Se Hinton Epub Download
Shanae Maerz
The present study investigated what complaints are prominent in psychologically distressed Vietnamese in Vietnam beyond standard symptoms assessed by Western diagnostic instruments for anxiety and depression. To form the initial Vietnamese Symptom and Cultural Syndrome Addendum (VN SSA), we reviewed the literature, consulted experts, and conducted focus groups. The preliminary VN SSA was then used in a general survey (N = 1004) of five provinces in Vietnam. We found that the VN SSA items were highly and significantly correlated with a measure of anxious-depressive psychopathology (a composite measure of the General Anxiety Disorder-7; Posttraumatic Diagnostic Scale; and Patient Health Questionnaire-9). The VN SSA item most highly correlated to anxious-depressive psychopathology was "thinking a lot" ( r = .54), reported by 15.8% of the sample. Many other symptoms in the addendum also were prominent, such as orthostatic dizziness (i.e., dizziness upon standing up; r = .41), reported by 22.9% of the sample. By way of comparison, somatic complaints more typically assessed to profile Western anxious-depressive distress, such as palpitations, were less prominent, as evidenced by being less strongly correlated to Western psychiatric symptoms and being less frequent (e.g., palpitations: r = .31, 7.1% of the sample). Study results suggest that to avoid category truncation when profiling anxious-depressive distress among Vietnamese that items other than those in standard psychopathology measures should also be assessed.
In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.
Relatively speaking, feature extraction is achieved in an automatic way throughout the DL algorithms. This encourages researchers to extract discriminative features using the smallest possible amount of human effort and field knowledge [18]. These algorithms have a multi-layer data representation architecture, in which the first layers extract the low-level features while the last layers extract the high-level features. Note that artificial intelligence (AI) originally inspired this type of architecture, which simulates the process that occurs in core sensorial regions within the human brain. Using different scenes, the human brain can automatically extract data representation. More specifically, the output of this process is the classified objects, while the received scene information represents the input. This process simulates the working methodology of the human brain. Thus, it emphasizes the main benefit of DL.
In the field of ML, DL, due to its considerable success, is currently one of the most prominent research trends. In this paper, an overview of DL is presented that adopts various perspectives such as the main concepts, architectures, challenges, applications, computational tools and evolution matrix. Convolutional neural network (CNN) is one of the most popular and used of DL networks [19, 20]. Because of CNN, DL is very popular nowadays. The main advantage of CNN compared to its predecessors is that it automatically detects the significant features without any human supervision which made it the most used. Therefore, we have dug in deep with CNN by presenting the main components of it. Furthermore, we have elaborated in detail the most common CNN architectures, starting with the AlexNet network and ending with the High-Resolution network (HR.Net).
Several published DL review papers have been presented in the last few years. However, all of them have only been addressed one side focusing on one application or topic such as the review of CNN architectures [21], DL for classification of plant diseases [22], DL for object detection [23], DL applications in medical image analysis [24], and etc. Although these reviews present good topics, they do not provide a full understanding of DL topics such as concepts, detailed research gaps, computational tools, and DL applications. First, It is required to understand DL aspects including concepts, challenges, and applications then going deep in the applications. To achieve that, it requires extensive time and a large number of research papers to learn about DL including research gaps and applications. Therefore, we propose a deep review of DL to provide a more suitable starting point from which to develop a full understanding of DL from one review paper. The motivation behinds our review was to cover the most important aspect of DL including open challenges, applications, and computational tools perspective. Furthermore, our review can be the first step towards other DL topics.
This section will present a background of DL. We begin with a quick introduction to DL, followed by the difference between DL and ML. We then show the situations that require DL. Finally, we present the reasons for applying DL.
Reinforcement Learning operates on interacting with the environment, while supervised learning operates on provided sample data. This technique was developed in 2013 with Google Deep Mind [55]. Subsequently, many enhanced techniques dependent on reinforcement learning were constructed. For example, if the input environment samples: \(x_t\sim \rho \), agent predict: and the received cost of the agent is , P here is the unknown probability distribution, then the environment asks a question to the agent. The answer it gives is a noisy score. This method is sometimes referred to as semi-supervised learning. Based on this concept, several supervised and unsupervised techniques were developed. In comparison with traditional supervised techniques, performing this learning is much more difficult, as no straightforward loss function is available in the reinforcement learning technique. In addition, there are two essential differences between supervised learning and reinforcement learning: first, there is no complete access to the function, which requires optimization, meaning that it should be queried via interaction; second, the state being interacted with is founded on an environment, where the input \(x_t\) is based on the preceding actions [9, 56].
Convolutional Operation: Initially, the CNN input format is described. The vector format is the input of the traditional neural network, while the multi-channeled image is the input of the CNN. For instance, single-channel is the format of the gray-scale image, while the RGB image format is three-channeled. To understand the convolutional operation, let us take an example of a \(4 \times 4\) gray-scale image with a \(2 \times 2\) random weight-initialized kernel. First, the kernel slides over the whole image horizontally and vertically. In addition, the dot product between the input image and the kernel is determined, where their corresponding values are multiplied and then summed up to create a single scalar value, calculated concurrently. The whole process is then repeated until no further sliding is possible. Note that the calculated dot product values represent the feature map of the output. Figure 8 graphically illustrates the primary calculations executed at each step. In this figure, the light green color represents the \(2 \times 2\) kernel, while the light blue color represents the similar size area of the input image. Both are multiplied; the end result after summing up the resulting product values (marked in a light orange color) represents an entry value to the output feature map.
Sometimes, the overall CNN performance is decreased as a result; this represents the main shortfall of the pooling layer, as this layer helps the CNN to determine whether or not a certain feature is available in the particular input image, but focuses exclusively on ascertaining the correct location of that feature. Thus, the CNN model misses the relevant information.
Parametric Linear Units: This is mostly the same as Leaky ReLU. The main difference is that the leak factor in this function is updated through the model training process. The parametric linear unit can be represented mathematically as in Eq. 7.