Plotting

Pattern Recognition: Instructional Materials


The Complete 2022 Android Machine Learning Course The Complete 2022 Android Machine Learning Course

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Welcome to The Complete 2021 Android Machine Learning Course. In this course, you will learn the use of Machine learning in Android along with training your own image recognition models for Android applications without knowing any background knowledge of machine learning. The course is designed in such a manner that you don't need any prior knowledge of machine learning to it. In modern world app development, the use of ML in mobile app development is compulsory. We hardly see an application in which ML is not being used.


Computer Vision - Richard Szeliski

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As humans, we perceive the three-dimensional structure of the world around us with apparent ease. Think of how vivid the three-dimensional percept is when you look at a vase of flowers sitting on the table next to you. You can tell the shape and translucency of each petal through the subtle patterns of light and shading that play across its surface and effortlessly segment each flower from the background of the scene (Figure 1.1). Looking at a framed group por- trait, you can easily count (and name) all of the people in the picture and even guess at their emotions from their facial appearance. Perceptual psychologists have spent decades trying to understand how the visual system works and, even though they can devise optical illusions1 to tease apart some of its principles (Figure 1.3), a complete solution to this puzzle remains elusive (Marr 1982; Palmer 1999; Livingstone 2008).


SAS Predictive Modeling

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You'll learn Understand the worth of this course of predictive modeling with SAS enterprise miner. Skills like skill to analyze data and see a complex pattern, coding skill, and strong understanding of concepts. Predictive modeling is the process of studying the data models. To predict models a different set of methods of statistics are used .these SAS enterprise miner tends to provide us with several tools for predictive modeling. By this course you will be able to have complete knowledge of predictive modeling with SAS enterprise miner.


Getting Started With Docker Image - Analytics Vidhya

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In this article we will be learning in depth about the nuances of how one can start with Docker and is it really important?r


Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence

arXiv.org Artificial Intelligence

Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.


Computer Vision: Python OCR & Object Detection Quick Starter

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This is the third course from my Computer Vision series. Image Recognition, Object Detection, Object Recognition and also Optical Character Recognition are among the most used applications of Computer Vision. Using these techniques, the computer will be able to recognize and classify either the whole image, or multiple objects inside a single image predicting the class of the objects with the percentage accuracy score. Using OCR, it can also recognize and convert text in the images to machine readable format like text or a document. Object Detection and Object Recognition is widely used in many simple applications and also complex ones like self driving cars.


Computer Vision: Python OCR & Object Detection Quick Starter

#artificialintelligence

This is the third course from my Computer Vision series. Image Recognition, Object Detection, Object Recognition and also Optical Character Recognition are among the most used applications of Computer Vision. Using these techniques, the computer will be able to recognize and classify either the whole image, or multiple objects inside a single image predicting the class of the objects with the percentage accuracy score. Using OCR, it can also recognize and convert text in the images to machine readable format like text or a document. Object Detection and Object Recognition is widely used in many simple applications and also complex ones like self driving cars.


Amazing AI: Reverse Image Search

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Artificial intelligence is one of the fastest growing fields of computer science today and the demand for excellent AI Engineers is increasing day in and day out. This course will help you stay competitive in the AI job market by teaching you how to create a Deep Learning End-to-End product on your own. Most courses focus on the basics of Deep Learning and teach you about the very basics of different models. In this course, however, you will learn how to write a whole End-to-End pipeline, from data preprocessing across choosing the right hyper-parameters, to showing your users results in a browser. The case that we will tackle in this course is an engine for Image to Image Search.


Image Recognition with Neural Networks From Scratch - CouponED

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Description This is an introduction to Neural Networks. The course explains the math behind Neural Networks in the context of image recognition. By the end of the course, we will have written a program in Python that recognizes images without using any autograd libraries. The only prerequisite is some high school precalculus.


Making machine learning trustworthy

Science

Machine learning (ML) has advanced dramatically during the past decade and continues to achieve impressive human-level performance on nontrivial tasks in image, speech, and text recognition. It is increasingly powering many high-stake application domains such as autonomous vehicles, self–mission-fulfilling drones, intrusion detection, medical image classification, and financial predictions ([ 1 ][1]). However, ML must make several advances before it can be deployed with confidence in domains where it directly affects humans at training and operation, in which cases security, privacy, safety, and fairness are all essential considerations ([ 1 ][1], [ 2 ][2]). The development of a trustworthy ML model must build in protections against several types of adversarial attacks (see the figure). An ML model requires training datasets, which can be “poisoned” through the insertion, modification, or removal of training samples with the purpose of influencing the decision boundary of a model to serve the adversary's intent ([ 3 ][3]). Poisoning happens when models learn from crowdsourced data or from inputs they receive while in operation, both of which are susceptible to tampering. Adversarially manipulated inputs can evade ML models through purposely crafted inputs called adversarial examples ([ 4 ][4]). For example, in an autonomous vehicle, a control model may rely on road-sign recognition for its navigation. By placing a tiny sticker on a stop sign, an adversary can evade the model to mistakenly recognize the stop sign as a yield sign or a “speed limit 45” sign, whereas a human driver would simply ignore the visually nonconsequential sticker and apply the brakes at the stop sign (see the figure). Attacks can also abuse the input-output interaction of a model's prediction interface to steal the ML model itself ([ 5 ][5], [ 6 ][6]). By supplying a batch of inputs (for example, publicly available images of traffic signs) and obtaining predictions for each, a model serves as a labeling oracle that enables an adversary to train a surrogate model that is functionally equivalent to the model. Such attacks pose greater risks for ML models that learn from high-stake data such as intellectual property and military or national security intelligence. ![Figure][7] Adversarial threats to machine learning Machine learning models are vulnerable to attacks that degrade model confidentiality and model integrity or that reveal private information. GRAPHIC: KELLIE HOLOSKI/ SCIENCE When models are trained for predictive analytics on privacy-sensitive data, such as patient clinical data and bank customer transactions, privacy is of paramount importance. Privacy-motivated attacks can reveal sensitive information contained in training data through mere interaction with deployed models ([ 7 ][8]). The root cause for such attacks is that ML models tend to “memorize” ancillary parts of their training data and, at prediction time, inadvertently divulge identifying details about individuals who contributed to the training data. One common strategy, called membership inference, enables an adversary to exploit the differences in a model's response to members and nonmembers of a training dataset ([ 7 ][8]). In response to these threats to ML models, the quest for countermeasures is promising. Research has made progress on detecting poisoning and adversarial inputs to limiting what an adversary may learn by just interacting with a model to limit the extent of model stealing or membership inference attacks ([ 1 ][1], [ 8 ][9]). One promising example is the formally rigorous formulation of privacy. The notion of differential privacy promises to an individual who participates in a dataset that whether your record belongs to a training dataset of a model or not, what an adversary learns about you by interacting with the model is basically the same ([ 9 ][10]). Beyond technical remedies, the lessons learned from the ML attack-defense arms race provide opportunities to motivate broader efforts to make ML truly trustworthy in terms of societal needs. Issues include how a model “thinks” when it makes decisions (transparency) and fairness of an ML model when it is trained to solve high-stake inference tasks for which bias exists if those decisions were made by humans. Making meaningful progress toward trustworthy ML requires an understanding about the connections, and at times tensions, between the traditional security and privacy requirements and the broader issues of transparency, fairness, and ethics when ML is used to address human needs. Several worrisome instances of biases in consequential ML applications have been documented ([ 10 ][11], [ 11 ][12]), such as race and gender misidentification, wrongfully scoring darker-skin faces for higher likelihood of being a criminal, disproportionately favoring male applicants in resume screenings, and disfavoring black patients in medical trials. These harmful consequences require that the developers of ML models look beyond technical solutions to win trust among human subjects who are affected by these harmful consequences. On the research front, especially for the security and privacy of ML, the aforementioned defensive countermeasures have solidified the understanding around blind spots of ML models in adversarial settings ([ 8 ][9], [ 9 ][10], [ 12 ][13], [ 13 ][14]). On the fairness and ethics front, there is more than enough evidence to demonstrate pitfalls of ML, especially on underrepresented subjects of training datasets. Thus, there is still more to be done by way of human-centered and inclusive formulations of what it means for ML to be fair and ethical. One misconception about the root cause of bias in ML is attributing bias to data and data alone. Data collection, sampling, and annotation play a critical role in causing historical bias, but there are multiple junctures in the data processing pipeline where bias can manifest. From data sampling to feature extraction, from aggregation during training to evaluation methodologies and metrics during testing, bias issues manifest across the ML data-processing pipeline. At present, there is a lack of broadly accepted definitions and formulations of adversarial robustness ([ 13 ][14]) and privacy-preserving ML (except for differential privacy, which is formally appealing yet not widely deployed). Lack of transferability of notions of attacks, defenses, and metrics from one domain to another is also a pressing issue that impedes progress toward trustworthy ML. For example, most ML evasion and membership inference attacks illustrated earlier are predominantly on applications such as image classification (road-sign detection by an autonomous vehicle), object detection (identifying a flower from a living room photo with multiple objects), speech processing (voice assistants), and natural language processing (machine translation). The threats and countermeasures proposed in the context of vision, speech, and text domain hardly translate to one another, often naturally adversarial domains, such as network intrusion detection and financial-fraud detection. Another important consideration is the inherent tension between some trustworthiness properties. For example, transparency and privacy are often conflicting because if a model is trained on privacy-sensitive data, aiming for the highest level of transparency in production would inevitably lead to leakage of privacy-sensitive details of data subjects ([ 14 ][15]). Thus, choices need to be made as to the extent that transparency is penalized to gain privacy, and vice versa, and such choices need to be made clear to system purchasers and users. Generally, privacy concerns prevail because of the legal implications if they are not enforced (for example, patient privacy with respect to the Health Insurance Portability and Accountability Act in the United States). Also, privacy and fairness may not always develop synergy. For example, although privacy-preserving ML (such as differential privacy) provides a bounded guarantee on indistinguishability of individual training examples, in terms of utility, research shows that minority groups in the training data (for example, based on race, gender, or sexuality) tend to be negatively affected by the model outputs ([ 15 ][16]). Broadly speaking, the scientific community needs to step back and align the robustness, privacy, transparency, fairness, and ethical norms in ML with human norms. To do this, clearer norms for robustness and fairness need to be developed and accepted. In research efforts, limited formulations of adversarial robustness, fairness, and transparency must be replaced with broadly applicable formulations like what differential privacy offers. In policy formulation, there needs to be concrete steps toward regulatory frameworks that spell out actionable accountability measures on bias and ethical norms on datasets (including diversity guidelines), training methodologies (such as bias-aware training), and decisions on inputs (such as augmenting model decisions with explanations). The hope is that these regulatory frameworks will eventually evolve into ML governance modalities backed by legislation to lead to accountable ML systems in the future. Most critically, there is a dire need for insights from diverse scientific communities to consider societal norms of what makes a user confident about using ML for high-stake decisions, such as a passenger in a self-driving car, a bank customer accepting investment recommendations by a bot, and a patient trusting an online diagnostic interface. Policies need to be developed that govern safe and fair adoption of ML in such high-stake applications. Equally important, the fundamental tensions between adversarial robustness and model accuracy, privacy and transparency, and fairness and privacy invite more rigorous and socially grounded reasonings about trustworthy ML. Fortunately, at this juncture in the adoption of ML, a consequential window of opportunity remains open to tackle its blind spots before ML is pervasively deployed and becomes unmanageable. 1. [↵][17]1. I. Goodfellow, 2. P. McDaniel, 3. N. Papernot , Commun. ACM 61, 56 (2018). [OpenUrl][18] 2. [↵][19]1. S. G. Finlayson et al ., Science 363, 1287 (2019). [OpenUrl][20][Abstract/FREE Full Text][21] 3. [↵][22]1. J. Langford, 2. J. Pineau 1. B. Biggio, 2. B. Nelson, 3. P. Laskov , Proceedings of the 29th International Conference on Machine Learning, Edinburgh, Scotland, UK, J. Langford, J. Pineau, Eds. (Omnipress, 2012), pp. 1807–1814. 4. [↵][23]1. K. Eykholt et al ., Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2018), pp. 1625–1634. 5. [↵][24]1. F. Tramèr, 2. F. Zhang, 3. A. Juels, 4. M. K. Reiter, 5. T. Ristenpart , Proceedings of the 25th USENIX Security Symposium, Austin, TX (USENIX Association, 2016), pp. 601–618. 6. [↵][25]1. A. Ali, 2. B. Eshete , Proceedings of the 16th EAI International Conference on Security and Privacy in Communication Networks, Washington, DC (EAI, 2020), pp. 318–338. 7. [↵][26]1. R. Shokri, 2. M. Stronati, 3. C. Song, 4. V. Shmatikov , Proceedings of the 2017 IEEE Symposium on Security and Privacy, San Jose, CA (IEEE, 2017), pp. 3–18. 8. [↵][27]1. N. Papernot, 2. M. Abadi, 3. U. Erlingsson, 4. I. Goodfellow, 5. K. Talwar , arXiv:1610.05755 [stat.ML] (2017). 9. [↵][28]1. I. Jarin, 2. B. Eshete , Proceedings of the 7th ACM International Workshop on Security and Privacy Analytics (2021), pp. 25–35. 10. [↵][29]1. J. Buolamwini, 2. T. Gebru , Proceedings of Conference on Fairness, Accountability and Transparency, New York, NY (ACM, 2018), pp. 77–91. 11. [↵][30]1. A. Birhane, 2. V. U. Prabhu , Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (IEEE, 2021), pp. 1537–1547. 12. [↵][31]1. N. Carlini et al ., arXiv:1902.06705 [cs.LG] (2019). 13. [↵][32]1. N. Papernot, 2. P. McDaniel, 3. A. Sinha, 4. M. P. Wellman , Proceedings of 3rd IEEE European Symposium on Security and Privacy (London, 2018), pp. 399–414. 14. [↵][33]1. R. Shokri, 2. M. Strobel, 3. Y. Zick , Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, New York, NY (2021); . 15. [↵][34]1. V. M. Suriyakumar, 2. N. Papernot, 3. A. Goldenberg, 4. M. Ghassemi , FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (ACM, 2021), pp. 723–734. 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