Artificial Intelligence

PyTorch announced first PyTorch Developer Day, starting 8 AM on November 12, 2020 PST. Learn more: https://pytorch.org/blog/pytorch-developer-day-2020/

🧐 Understanding how to build AI models is one thing. Understanding why AI models provide the results they provide is another. 🤓 Read this amazing blog 'Explaining the Explainable AI: A 2-Stage Approach', here.

Are you someone who aspires to build a career in Data Science? Join us for a ✨FREE Online Webinar on Saturday, 31st October 2020, 11:00 AM on Zoom to find about everything you need to know about pursuing Data Science from abroad - different fields, curriculum, costs, job prospects, and tips to apply. ◾️ Guest Speakers: Aditya Baser (Data Science Intern at ✨ HEXANIKA | Student at ✨ Columbia University) and Raghavendra Vedula (Machine Learning Researcher @ ISI | Student at University of Southern California) ◾️ Date: 31st October 2020 ◾️ Time: 11:00 AM ◾️ Venue: Zoom ✨ Limited slots available! Register now: https://bit.ly/2FXBleA 💫

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Some Notable Recent ML Papers and Future Trends by Aran Komatsuzaki, here is the link

Answers of yesterdays post is in this.

Bias-Variance Tradeoff! ⚡️ 👉🏼 The bias-variance trade-off is a useful conceptualization for selecting and configuring models, although generally cannot be computed directly as it requires full knowledge of the problem domain, which we do not have. 👉🏼 Nevertheless, in some cases, we can estimate the error of a model and divide the error down into bias and variance components, which may provide insight into a given model’s behavior. 🤓 In this tutorial, you will discover how to calculate the bias and variance for a machine learning model, with Python.

Dive into Deep Learning An interactive deep learning book with code, math, and discussions Provides NumPy/MXNet, PyTorch, and TensorFlow implementations Google Colab: https://d2l.ai/chapter_appendix-tools-for-deep-learning/colab.html Amazon sage maker: https://d2l.ai/chapter_appendix-tools-for-deep-learning/sagemaker.html Run Locally on Jupyter Notebook: https://d2l.ai/chapter_installation/index.html Personally I loved this book,because each section has an executable Jupyter notebook, where you can modify the code and tune hyperparameters to get instant feedback to accumulate practical experiences in deep learning

Hey Everyone! *TopCoders Club* is excited to host *Mr. Shreyansh Daftrey*, an *AI Research Scientist at NASA*, joining us straight from *NASA HEADQUARTERS*, for the next *TCx* event. He’ll talk about *his journey to NASA* and *how AI is being used in the 'Mars 2020 Perseverance Rover'!!* *Join us live on 18th October at 10:00AM IST* Register now: https://TCx.TopCoders.club For updates, join The TopCoders Community: https://topcoders.club/join

👩🏻‍💻 Why should one study Linear Algebra for ML? 👉🏼 Clearly, to develop a better intuition for machine learning and deep learning algorithms and not treat them as black boxes. This would allow you to choose proper hyper-parameters and develop a better model. You would also be able to code algorithms from scratch and make your own variations to them as well. 👉🏼 Learn Linear Algebra for Machine Learning with: Khan Academy: https://www.khanacademy.org/math/linear-algebra Udacity: https://www.udacity.com/course/linear-algebra-refresher-course--ud953 Coursera: https://www.coursera.org/learn/linear-algebra-machine-learning Here are some amazing freely available ebooks on the same topic: Mathematics for Machine Learning: https://mml-book.github.io/book/mml-book.pdf An Introduction to Statistical Learning: https://faculty.marshall.usc.edu/gareth-james/ISL/ Happy machine learning! 🎉

🦁 AI discovers that every lion has a unique and trackable roar: https://thenextweb.com/neural/2020/10/14/ai-discovers-that-every-lion-has-a-unique-and-trackable-roar/ What an interesting application of AI! 👀

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