The Visual Guide on How Neural Networks Learn from Data | Data Science Online Course by Udemy

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The BEST Resource for Understanding Neural Networks and How They Learn

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The Visual Guide on How Neural Networks Learn from Data

Course Achievements (January 2021):+3,100WorldwideStudents enrolledTrophy Awards for Key Section Achievements! Some Student Reviews are:”This is an excellent course for people who wish to get an introductory experience in learning about Neural Networks.” (December 2020).”This is a very good introduction on how ANN work. It helps build intuition both on the backpropagation and the math behind it.” (January 2020).”This course does what it claims to do very well.” (October 2019)”Very structured and logical” (July 2018).”Enlightening overview of how neural networks operate mathematically.” (March 2018).”I just loved this course. The course is very well taught and is divided in easy-to-digest units.”(December 2017).Hi. Thanks for showing interest in this course! What makes this course special: Step-by-Step Neural Network Learning Process, Master topics like Fundamentals, Objectives, Required Datasets, Weights, Biases, Nodes, Activation functions, Feed-Forward Passes, Predictions, Losses, Gradient Descent, Learning, Backpropagation and more! Plus, personalized feedback and help. You ask, Ianswer directly! This is your BEST resource for Neural Networks (NN) learning! A must for understanding special concepts and not get lost in computing your own NNs: First: You’ll start the Neural NetworksPrimer with Fundamentals, Objectives, Data and more: Learn concepts using analogies for maximum learning, so you will be fully covered. Learning how NNs learn will be easy with this Primer under your sleeve! Second: You’ll continue the NN Primer with Learning, Backpropagation and Predictions and more topicsIn aneasyandintuitiveway, you will understand how they work, This is fundamental in the NN Learning Process. At the end of this section, youwill have mastered the NN Primer! Now, you are ready for the Step-by-Step (in-Motion) sections! Third: You’ll start the in-Motion section with Inputs, Weights, Biases, Activations, Nodes and Feed-Forward Passes: See how they work inside an NN, Step-by-step templates, so you can follow every detail, These files will be dynamic, so you’ll understand how NNs work as numbers will be updated on-the-fly and right in front of your eyes. Forth: You’ll continue withthe in-Motionsection with NN Learning, Backpropagation, Tuning and Prediction: You will understand how NNs learn from the data. This all part of the dynamictemplates you get to keep. You’ll do several examples along the way for maximum learning. Lastly, you’ll see what NNs do to make the best predictions. Fifth: You’ll finish the in-Motion section by doing a complete rundown on everyting you’ve learned so far: You’ll see how all NN inner componentswork for learning and prediction. Pay close attention at how all parts adjust, making the NN learn in front of your eyes. After this section, you will be fully versed on how NNs learn! Sixth:I will devote a section for moreadditional knowledge and resources for continous learning. And then, I will conclude with some Final Words. What are the Requirements?Theonly thingyou’ll need for this course is: Excel and PowerPoint: It is that easy! You will also need to bring your Basic Maths too, If you bring your Calculus (Derivatives) knowledge, that will be a big plus for you (but not required),What are some of the Benefits?As it is usual in my courses, you will get all files and spreadsheets for all lectures. This way you can replicate everything I do immediately after each lecture. Neural Networks are the new thing today. With it, you can explore and engage Artificial Intelligence, which I recommend you to dive in as it’s part of the future. Plus, it’s very rewarding and fun too! New content coming in the near future, let me know yout thoughts. Lastly, you canpostquestions or doubts, and Ill answer to you personally.I hope you find this course as useful as I have creating it! Ill see you inside-M.A. Mauricio M.

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