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teddykoker.com | ||
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jaketae.github.io
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| | | | | Recently, a friend recommended me a book, Deep Learning with Python by Francois Chollet. As an eager learner just starting to fiddle with the Keras API, I decided it was a good starting point. I have just finished the first section of Part 2 on Convolutional Neural Networks and image processing. My impression so far is that the book is more focused on code than math. The apparent advantage of this approach is that it shows readers how to build neural networks very transparently. It's also a good introduction to many neural network models, such as CNNs or LSTMs. On the flip side, it might leave some readers wondering why these models work, concretely and mathematically. This point notwithstanding, I've been enjoying the book very much so far, and this post is... | |
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dhruvs.space
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| | | | | This is the second post in this series on the basics of Machine Learning. The last post detailed the functioning of an artificial neuron, and how it can be trained to linearly segment a dataset. However, most real world datasets are not linearly separable, which begs the question: What is the point of learning about a neuron? Well, by the end of this post, we'll see that a bunch of neurons, when stacked together, can learn to create powerful non-linear solution spaces. Let's see how that works. | |
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www.arrsingh.com
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| | | | | Linear Regression predicts the value of a dependent variable (y) given one or more independent variables (x1, x2, x3...xn). In this case, y is continuous - i.e. it can hold any value. In many real world problems[1], however, we often want to predict a binary value instead | |
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www.nicktasios.nl
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| | | In the Latent Diffusion Series of blog posts, I'm going through all components needed to train a latent diffusion model to generate random digits from the MNIST dataset. In the second post, we will bu | ||
