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This is my solution to predictOneVsAll.m function in Programming assignment 3 from the famous Machine Learning course by Andrew Ng.

Github: https://github.com/AladdinPerz....on/Courses/tree/mast

Implementation in Python of the weighted interval scheduling problem in Python using dynamic programming. I try to keep the code as clean as possible and hopefully it's crystal clear for you guys!

Code repository:

https://github.com/AladdinPerz....on/Algorithms-Collec


Explanation of algorithm: https://youtu.be/iIX1YvbLbvc

Welcome to part 2 of the web scraping with Beautiful Soup 4 tutorial mini-series. In this tutorial, we're going to talk about navigating source code to get just the slice of data we want.

Tutorial code: https://pythonprogramming.net/....navigating-pages-scr

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Do you think that 8k movies will become the norm in the very near future?...well i don't and heres why! But be fore you go; Don't forget to Like, Comment & Subscribe!!

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This Edureka video on "Introduction to Tableau" will tell you how powerful this data visualization tool is and how it works. Tableau is one of the "must learn" tool in today's IT world. This introductory video talks about:

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What is Tableau?

Tableau is one of the powerful and rapidly growing data visualization tools used in the Business Intelligence industry. It simplifies and provides a pictorial representation of raw data into an understandable format without technical and coding knowledge.

How does a beginner learn Tableau?

Tableau has become one of the most valuable technologies in demand. Edureka's Tableau eLearning Course is designed by compiling an extensive course on Tableau for beginners, which will help you learn how to build visualizations, design dashboards, and organize data to make smarter business decisions. This Tableau Certification course will prepare you for the Tableau Desktop Certified Associate exam.

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Can we make neural networks using light? From spatial light modulators to phase-change materials, we're diving into optical neural networks. Sign up for CuriosityStream and Nebula at https://curiositystream.com/jordan to get access to the next journal club on optical neural networks!

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Sources:

Cheng, T. Y., Chou, D. Y., Liu, C. C., Chang, Y. J., & Chen, C. C. (2019). Optical neural networks based on optical fiber-communication system. Neurocomputing, 364, 239–244. https://doi.org/10.1016/j.neucom.2019.07.051

Sui, X., Wu, Q., Liu, J., Chen, Q., & Gu, G. (2020). A review of optical neural networks. IEEE Access, 8, 70773–70783. https://doi.org/10.1109/ACCESS.2020.2987333

Zhou, T., Fang, L., Yan, T., Wu, J., Li, Y., Fan, J., … Dai, Q. (2020). In situ optical backpropagation training of diffractive optical neural networks. Photonics Research, 8(6), 940. https://doi.org/10.1364/prj.389553

Lin, X., Rivenson, Y., Yardimci, N. T., Veli, M., Luo, Y., Jarrahi, M., & Ozcan, A. (2018). All-optical machine learning using diffractive deep neural networks. Science, 361(6406), 1004–1008. https://doi.org/10.1126/science.aat8084

Lu, T. T. (1990). Self-organizing optical neural network for unsupervised learning. Optical Engineering, 29(9), 1107. https://doi.org/10.1117/12.55702

Casasent, D. P., & Barnard, E. (1990). Adaptive-clustering optical neural net. Applied Optics, 29(17), 2603. https://doi.org/10.1364/ao.29.002603

Abu-Mostafa, Y., & Psaltis, D. (1987). Optical Neural Computers. Scientific American, 256(3). https://doi.org/10.2307/24979343

Wilson, C. L., Watson, C. I., & Paek, E. G. (2000). Effect of resolution and image quality on combined optical and neural network fingerprint matching. Pattern Recognition, 33(2), 317–331. https://doi.org/10.1016/S0031-3203(99)00052-7

Lee, L.-S., Stoll, H. M., & Tackitt, M. C. (1989). Continuous-time optical neural network associative memory. Optics Letters, 14(3), 162. https://doi.org/10.1364/ol.14.000162

Bergeron, A., Arsenault, H. H., Eustache, E., & Gingras, D. (1994). Optoelectronic thresholding module for winner-take-all operations in optical neural networks. Applied Optics, 33(8), 1463. https://doi.org/10.1364/ao.33.001463

Caulfield, H. J., Kinser, J., & Rogers, S. K. (1989). Optical Neural Networks. Proceedings of the IEEE, 77(10), 1573–1583. https://doi.org/10.1109/5.40669

Shariv, I., & Friesem, A. A. (1989). All-optical neural network with inhibitory neurons. Optics Letters, 14(10), 485. https://doi.org/10.1364/ol.14.000485

https://qz.com/852770/theres-a....-limit-to-how-small-

https://ieeexplore.ieee.org/document/9064516

https://www.nature.com/articles/s41586-019-1157-8

https://iopscience.iop.org/art....icle/10.1088/2040-89

https://www.osapublishing.org/....optica/abstract.cfm?

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This video is part of my Introduction of Deep Learning course.

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We are going to talk about the internal workings of ChatGPT and the fundamental concepts it lies on: Language Models, Transformer Neural Networks, GPT models and Reinforcement Learning

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Transformer Neural Networks: https://www.youtube.com/watch?v=TQQlZhbC5ps

RESOURCES
[1] ChatGPT blog: https://openai.com/blog/chatgpt/
[2] Instruct GPT which is the model ChatGPT was modeled after: https://arxiv.org/pdf/2203.02155.pdf
[3] Proximal Policy Optimization is how ChatGPT makes use of human rankings to update model parameters and make it more "safe" and "truthful": https://openai.com/blog/openai-baselines-ppo/
[4] Here is a paper that shows how Reinforcement learning through human feedback actually helps: https://arxiv.org/pdf/2009.01325.pdf
[5] Every timestep, a subword token is generated. Here is some more information on this process with BPE: https://towardsdatascience.com..../byte-pair-encoding-
[6] Basic Concepts in Reinforcement Learning: https://www.baeldung.com/cs/ml....-policy-reinforcemen
[7] Why Does GPT-3 write non-sensical stuff that sounds legit? https://www.alignmentforum.org..../posts/BgoKdAzogxmgk
[8] What is GPT-3.5? https://beta.openai.com/docs/m....odel-index-for-resea



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