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Build a deep neural network in 4 mins with TensorFlow in Colab

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What is a Neural Network – Ep. 2 (Deep Learning SIMPLIFIED)

If you’ve been ignoring neural nets cuz you think they’re too hard to understand or you think you don’t need them…boy do I have a treat for you! In this video you’ll learn about neural nets without any of the math or code – just an intro to what they
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Neural Networks: Crash Course Statistics #41

Hi, I’m Adriene Hill, and Welcome back to Crash Course Statistics. In previous episodes we’ve talked about things like cars learning how to drive themselves…and apps that can recognize handwriting and turn it into printed text. A lot of these projects are done using a type of Machine Learning called
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How to Design a Convolutional Neural Network | Lecture 8

One of the questions that I get frequently is ‘how do you design a neural network’ or more specifically ‘how do you know how many layers you need to have’ or ‘how do you know what’s the right value for a particular hyperparameter’. First things first, if you are not
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Inside a Neural Network – Computerphile

Let’s start talking about convolutional neural networks again A few people have been asking what does the convolutional layers look like So you know, what transformations are happening on these input images, that mean we can do something interesting in terms of machine learning. So, what I’ve done is that
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Neural Networks and Deep Learning: Crash Course AI #3

Hi, I’m Jabril, and welcome to CrashCourse AI! In the supervised learning episode, we taught John Green-bot to learn using a perceptron, a program that imitates one neuron. But our brains make decisions with 100 billion neurons, which have trillions of connections between them! We can actually do a lot
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Gradient descent, how neural networks learn | Deep learning, chapter 2

Last video I laid out the structure of a neural network I’ll give a quick recap here just so that it’s fresh in our minds And then I have two main goals for this video. The first is to introduce the idea of gradient descent, which underlies not only how
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