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Brief profile: Science student in school , Engineering in Computer Science,Zero experience of Economics, Finance, Management. Never gave any Banking exam. Non-upsc aspirant. Have been working in Coal India Ltd. since 2012.Sources of my preparation:-(*UT means Youtube)PHASE-11. Current affairs:a) Study iq: Gaurav Garg's daily CA, Best 200 mcq series.(UT)b) Adda: Daily MCQ practice.(App)c) GK today: Daily MCQ practice(App)d) Cloud affairs: Daily MCQ practice(App)Combining all, made my own notes category-wise(like appointment, sports, author, states etc) for every month. (6 months)Categories based on which I made my monthwise notes are as follows:Regarding Monthwise State News, I used to mark them in Map which helped me a lot to retain in the memory. Please see below:The Fruit of hardwork is always good:Biswa's answer to How can I improve my GA score in RBI Grade B? I got only 25/80 marks in GA in the 2019 exam.2. Reasoning:a) Whiteboard classes: Tarun Jha(UT)Puzzles: I found them difficult to solve faster, so neither practiced nor attempted in exam. Did other topics except Puzzles.3. Quanta) White board classes: Tarun Jha (UT)b) Edutap Quant class: Ankur Chaudhari (Edutap)4. Englisha) Practised only through Mock tests (Edutap)Gave 10 phase-1 Mock tests before exam.(Edutap)PHASE-2Economics:-1. Mrunal (All Basic statics): started economics journey with his lectures(UT)2. Edutap (Static + Current affairs)4. Civils Tap (for govt schemes) (UT)5. Vision IAS pdf ( Current affairs + Govt schemes)6. Vikaspedia (Govt schemes)7. Ministry websites(Govt schemes)8. PIB(Govt schemes)9. Budget, Eco Survey, Census etc: UT + Edutap NotesMade own notes for Static & Govt schemes seperately. Revised Current affairs mcqs in Ipad.ESI Current affair strategy:Biswa's answer to What was your strategy to prepare for the ESI current affairs as well as sources?Dont go very deep into theories for these topics: Sustainable dev, IMF, WB, Reg. Coop,WTO, Industrial, Labour policy. RBI asks Current affairs from these topics, so dont waste much time in theory like definition, history etc.State Govt / Central Govt schemes:Biswa's answer to Is it necessary to read schemes and initiatives launched by state governments for the RBI grade B exam, or would central level schemes suffice?Finance-1. Sunny Gulve: Markets (UT)2. CA. Naresh Aggarwal : Numericals(UT)3. Unicom : Numericals (UT)3. Prasanna Chandra: Numericals(Book)4. Edutap Notes (Static + Current affairs)5. Edutap: Imp RBI notifications of last 6 months.6. Google, Investopedia, Wiki.Made my own notes seperately for static, numericals & RBI notifications.Syllabus of ESI & FIN:Management:-1. Study dot com2. Edutap Notes3. Dr. Amit Lal(UT)4. Iken Edu(UT)5. Mind your own business(UT)6. Organizational Communication Channel(UT)7. EasyMBA(UT)8. Students can I help you?(UT)9. Wikipedia, Google etc.Topic wise made my own notes in a single diary (one time effort). Revised the diary before exam.Detailed FM strategy can be viewed in the link below:Biswa's answer to How do I prepare Finance and Management for RBI Grade B Exam?English:-Learnt the rules of writing Essay, Precis & Comprehension from Edutap. Practiced few Essays & Precis before Mains.Newspaper: The Hindu. Editorials & Economic news.Biswa's answer to Can newspaper reading be skipped in preparation for the RBI grade B exams? I plan to use a daily current affairs PDF from 2 online providers.Editorials helped in developing views which was useful in English paper & Interview.These are the sources which I can remember, if I miss any, I shall add them later.My Interview experience is in the link below:Biswa's answer to How was your Rbi grade b officer interview experience?.Sources may vary but Hardwork, patience is uniform. Thank you & Best of luck to all the future aspirants.At the end I want to Thank all my Teachers who helped me to sail through 🙏🙏. Sharing some messages with you.

This is a good question. I am uncertain how you can begin applying deep learning in robotics. I did find a research article regarding deep learning I hope will be useful. I have not heard of robotic applications. You can apply to several universities that teach this subject.KoreaMed SynapseDeep learningIan Goodfellow, Yoshua Bengio, Aaron Courville, Yoshua BengioMIT press, 2016Kwang Gi Kim https://doi. org/10.4258/hir. 2016.22. 4.351 ing those who are beginning their careers in deep learning and artificial intelligence research. The other target audience consists of software engineers who may not have a background in machine learning or statistics but who nonetheless want to acquire this knowledge rapidly and begin using deep learning in their fields. Deep learning has already proven useful in many software disciplines, including computer vision, speech and audio processing, natural language processing, robotics, bioinformatics and chemistry, video games, search engines, online advertising and finance. This book has been organized into three parts so as to best accommodate a variety of readers. In Part I, the author intro duces basic mathematical tools and machine learning concepts. Part II describes the most established deep learning algorithms that are essentially solved technologies. Part III describes more speculative ideas that are widely believed to be important for future research in deep learning. In this book, certain areas assume that all readers have a computer science background. The authors assume familiarity with programming and a basic understanding of computational performance issues, complexity theory, introductory-level calculus and some of the terminology of graph theory.View at synapse.koreamed.org[PDF] koreamed.orgCited by 2811Related articlesAll 10 versionsnow publishers - HomeDeep learning: methods and applicationsLi Deng, Dong YuFoundations and Trends® in Signal Processing 7 (3–4), 197-387, 2014This monograph provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria in mind:(1) expertise or knowledge of the authors;(2) the application areas that have already been transformed by the successful use of deep learning technology, such as speech recognition and computer vision; and (3) the application areas that have the potential to be impacted significantly by deep learning and that have been experiencing research growth, including natural language and text processing, information retrieval, and multimodal information processing empowered by multi-task deep learning.View at nowpublishers.com[PDF] nowpublishers.comCited by 1022Related articlesAll 20 versionsMachine learning, a probabilistic perspectiveChristian RobertCHANCE 27 (2), 62-63, 2014The book also introduces the notion of a Bayesian likelihood function (p. 228), which “differs slightly from that in classical statistics.” The only difference I can spot is in the interpretation: Both functions of (θ, x) are numerically the same. Overall, the chapter on Bayesian inference does not spend much time on prior specification. There is a section on conjugate priors that does not mention picking the hyperparameters. While improper priors are introduced as limits of proper priors and as conveying “the least amount of information about [the parameters]”(p. 236), the difficulty in using improper priors for hypothesis testing is not mentioned. Both Chib’s method and the Savage-Dickey density ratio are suggested for the approximation of marginal likelihoods.View at amstat.tandfonline.comCited by 3521Related articlesAll 8 versionsScholarYEAR[PDF] koreamed.org[B] Deep learningI Goodfellow, Y Bengio, A Courville, Y Bengio - 2016 - KoreaMed Synapse… Practical Methodology Successfully applying deep learning techniques requires more than merelyknowing what … Correct application of an algorithm depends on mastering some fairly simplemethodology … Applications In this chapter, the authors describe how to use deep learn …Cited by 2811 Related articles[PDF] nowpublishers.comDeep learning: methods and applicationsL Deng, D Yu - Foundations and Trends® in Signal …, 2014 - now publishers - Home… already been transformed by the successful use of deep learning technology, such as speechrecognition and com- puter vision; and (3) the application areas that … These researchers havedemonstrated empirical successes of deep learning in diverse applications of computer …Cited by 1022 Related articlesMachine learning, a probabilistic perspectiveC Robert - 2014 - http://amstat.tandfonline.comPage 1. VOL. 27.2, 2014 62 Machine Learning, a Probabilistic Perspective Kevin P. Murphy … Ihave to admit the rather embarrassing fact that Machine Learning, a Probabilistic Perspective is the first machine learning book I have read in full detail …Cited by 3521 Related articles[PDF] toronto.eduA fast learning algorithm for deep belief netsGE Hinton, S Osindero, YW Teh - Neural computation, 2006 - MIT Press… Section 4 introduces a fast, greedy learning algorithm for constructing multilayer directed networks one layer … The “weak” learner that is used to construct deep directed nets is itself an … by the bestbackpropagation nets when they are not handcrafted for this particular application …Cited by 8234 Related articles[PDF] arxiv.orgDeep learning for detecting robotic graspsI Lenz, H Lee, A Saxena - … International Journal of Robotics …, 2015 - http://journals.sagepub.com… INSTITUTIONAL ACCESS. Institution. Institutional Access. Shibboleth. Open Athens. Need Help? The International Journal of Robotics Research. Multimedia Archives 5.301. Impact Factor … Journal, New Content, Announcements. The International Journal of Robotics Research,Cited by 381 Related articles[PDF] nowpublishers.comLearning deep architectures for AIY Bengio - Foundations and trends® in Machine Learning, 2009 - now publishers - Home… guiding the training of intermediate levels of represen- tation using unsupervised learning, which can be … that exploit the same principle [131, 202] (see Section 4). Since 2006, deep networks have … can be trained with unlabeled data, in many of the above applications, they have …Cited by 5381 Related articles[PDF] researchgate.netDeep machine learning-a new frontier in artificial intelligence research [research frontier]I Arel, DC Rose, TP Karnowski - IEEE computational …, 2010 - IEEE Xplore Digital Library… deep learning methods in a variety of application domains. In addition to the MNIST handwritingchallenge [27], there are applications in face detection [10] [51], speech recognition and detection [12], general object recognition [9], natural language processing [24], and robotics …Cited by 591 Related articles[PDF] diva-portal.orgA review of unsupervised feature learning and deep learning for time-series modelingM Längkvist, L Karlsson, A Loutfi - Pattern Recognition Letters, 2014 - Elsevier… The key for any successful application lies in choosing the right representation … feature learning have shown to be successful at learning layers of feature representations for static data sets and can be combined with deep networks to create more powerful learning models …Cited by 240 Related articles[PDF] aaai.orgDeSTIN: A Scalable Deep Learning Architecture with Application to High-Dimensional Robust Pattern Recognition.I Arel, DC Rose, R Coop - AAAI Fall Symposium: Biologically Inspired …, 2009 - Association for the Advancement of Artificial IntelligenceI hope this is hkelpful. Thank you for this opportunity to resond to your question.Richard Cummings