Material UI is an open-source React component library that implements Google's Material Design. It's comprehensive and can be used in production out of the box.
\n","userId":"33d22b0e-9474-46b3-9da4-b1fb5d273abc","userDisplayName":"adixchen","public":true,"likeCount":0,"youtubeVideoId":null,"stackoverflowQuestionId":null,"createdAt":"2026-03-25T09:27:03.033Z","updatedAt":"2026-03-25T09:28:43.412Z"},{"_id":"69c0071876e96e382d449526","type":"bookmark","tags":["neural-networks","ai-learning"],"name":"GANs for Good- A Virtual Expert Panel by DeepLearning.AI - YouTube","location":"https://www.youtube.com/watch?v=9d4jmPmTWmc","language":"en","publishedOn":null,"sourceCodeURL":"","description":"To celebrate the launch of GANs Specialization, we’ve assembled a panel of GANs experts. They will discuss some of their current projects and the importance and future of GANs and also provide practical career advice for ML practitioners. ","descriptionHtml":"To celebrate the launch of GANs Specialization, we’ve assembled a panel of GANs experts. They will discuss some of their current projects and the importance and future of GANs and also provide practical career advice for ML practitioners.
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\n","userId":"33d22b0e-9474-46b3-9da4-b1fb5d273abc","userDisplayName":"adixchen","public":true,"likeCount":0,"youtubeVideoId":null,"stackoverflowQuestionId":null,"createdAt":"2026-03-15T12:28:24.422Z","updatedAt":"2026-03-15T12:28:27.651Z"},{"_id":"69b5a19176e96e382d447f75","type":"bookmark","tags":["ai-learning","cnn","neural-networks","deep-learning","computer-vision"],"name":"CS231n Deep Learning for Computer Vision","location":"https://cs231n.github.io/convolutional-networks/","language":"en","publishedOn":null,"sourceCodeURL":"","description":"Convolutional Neural Networks are very similar to ordinary Neural Networks from the previous chapter: they are made up of neurons that have learnable weights and biases. Each neuron receives some inputs, performs a dot product and optionally follows it with a non-linearity. The whole network still expresses a single differentiable score function: from the raw image pixels on one end to class scores at the other. And they still have a loss function (e.g. SVM/Softmax) on the last (fully-connected) layer and all the tips/tricks we developed for learning regular Neural Networks still apply.\n\nSo what changes? ConvNet architectures make the explicit assumption that the inputs are images, which allows us to encode certain properties into the architecture. These then make the forward function more efficient to implement and vastly reduce the amount of parameters in the network.","descriptionHtml":"Convolutional Neural Networks are very similar to ordinary Neural Networks from the previous chapter: they are made up of neurons that have learnable weights and biases. Each neuron receives some inputs, performs a dot product and optionally follows it with a non-linearity. The whole network still expresses a single differentiable score function: from the raw image pixels on one end to class scores at the other. And they still have a loss function (e.g. SVM/Softmax) on the last (fully-connected) layer and all the tips/tricks we developed for learning regular Neural Networks still apply.
\nSo what changes? ConvNet architectures make the explicit assumption that the inputs are images, which allows us to encode certain properties into the architecture. These then make the forward function more efficient to implement and vastly reduce the amount of parameters in the network.
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A neural network is a subset of machine learning that uses the interconnected layers of nodes to process the data and find patterns. These patterns or meaningful insights help us in strategic decision-making for various use cases. PyTorch is a Deep-learning framework that allows us to do this.
\nIt includes various modules for creating and training the neural networks. Among these, torch.nn and torch.nn.functional are popular. Let us discuss them in more detail in this article.
\n","userId":"33d22b0e-9474-46b3-9da4-b1fb5d273abc","userDisplayName":"adixchen","public":true,"likeCount":0,"youtubeVideoId":null,"stackoverflowQuestionId":null,"createdAt":"2026-03-11T12:13:44.700Z","updatedAt":"2026-03-11T12:13:44.700Z"},{"_id":"69afd64a23b88a381b5a1264","type":"bookmark","tags":["python","torchvision","docs"],"name":"Transforming images, videos, boxes and more — Torchvision documentation","location":"https://docs.pytorch.org/vision/stable/transforms.html","language":"en","publishedOn":null,"sourceCodeURL":"","description":"### Transforming images, videos, boxes and more\n\nTorchvision supports common computer vision transformations in the `torchvision.transforms.v2` module. Transforms can be used to transform and augment data, for both training or inference. The following objects are supported:\n- Images as pure tensors, Image or PIL image\n- Videos as Video\n- Axis-aligned and rotated bounding boxes as BoundingBoxes\n- Segmentation and detection masks as Mask\n- KeyPoints as KeyPoints.","descriptionHtml":"Torchvision supports common computer vision transformations in the torchvision.transforms.v2 module. Transforms can be used to transform and augment data, for both training or inference. The following objects are supported:
Torchvision provides many built-in datasets in the torchvision.datasets module, as well as utility classes for building your own datasets.
Here you find datasets for:
\nThis blog will explore the basic principles behind autoencoders, how they work, and their powerful application in modern machine learning. We’ll also discuss key concepts in a discussion format so you can understand the importance of this unsupervised learning approach.
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Whether you’re troubleshooting vanishing gradients, experimenting with new optimization techniques, or fine-tuning your model for production, mastering this one line of code can make all the difference.
\nThis guide assumes you’ve worked with PyTorch before and are familiar with basic training loops. Together, we’ll go beyond the basics and uncover what happens when optimizer.step() gets called — and why that matters.
By the end of this guide, you’ll walk away with:
\nThis section runs through the API for common tasks in machine learning. Refer to the links in each section to dive deeper.
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Interactive deep learning book with code, math, and discussions
\nImplemented with PyTorch, NumPy/MXNet, JAX, and TensorFlow
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\n","userId":"33d22b0e-9474-46b3-9da4-b1fb5d273abc","userDisplayName":"adixchen","public":true,"likeCount":0,"youtubeVideoId":null,"stackoverflowQuestionId":null,"createdAt":"2026-01-05T19:01:15.067Z","updatedAt":"2026-01-05T19:01:15.067Z"},{"_id":"695b779623b88a381b58fba4","type":"bookmark","tags":["ai-learning","ai","machine-learning","neural-networks"],"name":"But what is a neural network? | Deep learning chapter 1 (part of a list) - 18min","location":"https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi","language":"en","publishedOn":"2017-10-05T00:00:00.000Z","sourceCodeURL":"","description":"What are the neurons, why are there layers, and what is the math underlying it?\nHelp fund future projects: https://www.patreon.com/3blue1brown\nWritten/interactive form of this series: https://www.3blue1brown.com/topics/neural-networks\n\nFor those who want to learn more, I highly recommend the book by Michael Nielsen that introduces neural networks and deep learning: https://goo.gl/Zmczdy\n\nThere are two neat things about this book. First, it's available for free, so consider joining me in making a donation to Nielsen if you get something out of it. And second, it's centered around walking through some code and data, which you can download yourself, and which covers the same example that I introduced in this video. Yay for active learning!\nhttps://github.com/mnielsen/neural-networks-and-deep-learning","descriptionHtml":"What are the neurons, why are there layers, and what is the math underlying it?\nHelp fund future projects: https://www.patreon.com/3blue1brown\nWritten/interactive form of this series: https://www.3blue1brown.com/topics/neural-networks
\nFor those who want to learn more, I highly recommend the book by Michael Nielsen that introduces neural networks and deep learning: https://goo.gl/Zmczdy
\nThere are two neat things about this book. First, it's available for free, so consider joining me in making a donation to Nielsen if you get something out of it. And second, it's centered around walking through some code and data, which you can download yourself, and which covers the same example that I introduced in this video. Yay for active learning!\nhttps://github.com/mnielsen/neural-networks-and-deep-learning
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\n","userId":"33d22b0e-9474-46b3-9da4-b1fb5d273abc","userDisplayName":"adixchen","public":true,"likeCount":0,"youtubeVideoId":"W7-lsoL-Gi8","stackoverflowQuestionId":null,"createdAt":"2025-12-12T10:28:07.884Z","updatedAt":"2025-12-12T10:28:07.884Z"},{"_id":"6937bc3a23b88a381b58a8b8","type":"bookmark","tags":["paper","ai","machine-learning","ai-learning","deep-learning"],"name":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","location":"https://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf","language":"en","publishedOn":null,"sourceCodeURL":"","description":"**Abstract**\n\nDeep neural nets with a large number of parameters are very powerful machine learning\nsystems. However, overfitting is a serious problem in such networks. Large networks are also\nslow to use, making it difficult to deal with overfitting by combining the predictions of many\ndifferent large neural nets at test time. Dropout is a technique for addressing this problem.\nThe key idea is to randomly drop units (along with their connections) from the neural\nnetwork during training. This prevents units from co-adapting too much. During training,\ndropout samples from an exponential number of different “thinned” networks. At test time,\nit is easy to approximate the effect of averaging the predictions of all these thinned networks\nby simply using a single unthinned network that has smaller weights. This significantly\nreduces overfitting and gives major improvements over other regularization methods. We\nshow that dropout improves the performance of neural networks on supervised learning\ntasks in vision, speech recognition, document classification and computational biology,\nobtaining state-of-the-art results on many benchmark data sets.\nKeywords: neural networks, regularization, model combination, deep learning\n","descriptionHtml":"Abstract
\nDeep neural nets with a large number of parameters are very powerful machine learning\nsystems. However, overfitting is a serious problem in such networks. Large networks are also\nslow to use, making it difficult to deal with overfitting by combining the predictions of many\ndifferent large neural nets at test time. Dropout is a technique for addressing this problem.\nThe key idea is to randomly drop units (along with their connections) from the neural\nnetwork during training. This prevents units from co-adapting too much. During training,\ndropout samples from an exponential number of different “thinned” networks. At test time,\nit is easy to approximate the effect of averaging the predictions of all these thinned networks\nby simply using a single unthinned network that has smaller weights. This significantly\nreduces overfitting and gives major improvements over other regularization methods. We\nshow that dropout improves the performance of neural networks on supervised learning\ntasks in vision, speech recognition, document classification and computational biology,\nobtaining state-of-the-art results on many benchmark data sets.\nKeywords: neural networks, regularization, model combination, deep learning
\n","userId":"33d22b0e-9474-46b3-9da4-b1fb5d273abc","userDisplayName":"adixchen","public":true,"likeCount":0,"youtubeVideoId":null,"stackoverflowQuestionId":null,"createdAt":"2025-12-09T06:05:46.233Z","updatedAt":"2025-12-09T06:05:46.233Z"},{"_id":"68ef649d76e96e382d42293c","type":"bookmark","tags":["pytorch","python","ai","artificial-intelligence","deep-learning","machine-learning"],"name":"What is PyTorch? (Machine/Deep Learning) - 11min","location":"https://www.youtube.com/watch?v=fJ40w_2h8kk","language":"en","publishedOn":"2023-10-11T00:00:00.000Z","sourceCodeURL":"","description":"Very high level overview of pytorch and its capabailities.\n\nPyTorch is a popular open-source framework for machine learning and deep learning, backed by the PyTorch Foundation. It simplifies data preparation, model building, and training with features like data loaders, pre-defined layers, automatic gradient calculation, and optimization functions. In this video, PyTorch contributor Sahdev Zala and Distinguished Engineer Brad Topol present PyTorch's key features and explain why you should consider becoming a contributor.","descriptionHtml":"Very high level overview of pytorch and its capabailities.
\nPyTorch is a popular open-source framework for machine learning and deep learning, backed by the PyTorch Foundation. It simplifies data preparation, model building, and training with features like data loaders, pre-defined layers, automatic gradient calculation, and optimization functions. In this video, PyTorch contributor Sahdev Zala and Distinguished Engineer Brad Topol present PyTorch's key features and explain why you should consider becoming a contributor.
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\nIn this video, certification and cybersecurity expert Mike Chapple breaks down the basics of DMARC, DKIM and SPF to help you prepare for your exam.
\nIt shows example based on linkedin.com domain with the help of the dig tool
In this video, I explain to you which DNS records you should configure to run a fully functioning mail server with a good reputation. #DNS #MailServer #HomeLab
\nDOCS: https://github.com/christianlempa/cheat-sheets/blob/main/networking/dns-record-mailserver.md
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One of the Angular features that help us the most in building reusable components is Content Projection and ng-content.
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In JavaScript, the # symbol, also known as a hash or pound sign, holds a special significance. It is used to denote private fields in JavaScript classes. Now, you might be thinking, what are private fields?
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Workbooks provide a flexible canvas for data analysis and the creation of rich visual reports within the Azure portal. They allow you to tap into multiple data sources from across Azure and combine them into unified interactive experiences. Workbooks let you combine multiple kinds of visualizations and analyses, making them great for freeform exploration.
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