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91-days-algorithm

91天学算法-Leetcode图解题解集合(JavaScript/Python)(持续更新) Solutions and Explainations with Hand Drawings in Chinese(JavaScript/Python)(Constant Update)

architect-awesome

后端架构师技术图谱

databricks-llm-course-edx

My answers to the course projects

deep-learning-in-production

In this repository, I will share some useful notes and references about deploying deep learning-based models in production.

ds-take-home

My solution to the book A Collection of Data Science Take-Home Challenges

leetcode-1

LeetCode Solutions: A Record of My Problem Solving Journey.( leetcode题解，记录自己的leetcode解题之路。)

machine-learning

machine_learning_course

Machine learning course at UCF

mlquestions

Machine Learning and Computer Vision Engineer - Technical Interview Questions

mnist_gan

In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits! GANs were first reported on in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exploded in popularity. Here are a few examples to check out: Pix2Pix CycleGAN & Pix2Pix in PyTorch, Jun-Yan Zhu A list of generative models The idea behind GANs is that you have two networks, a generator 𝐺 and a discriminator 𝐷 , competing against each other. The generator makes "fake" data to pass to the discriminator. The discriminator also sees real training data and predicts if the data it's received is real or fake. The generator is trained to fool the discriminator, it wants to output data that looks as close as possible to real, training data. The discriminator is a classifier that is trained to figure out which data is real and which is fake. What ends up happening is that the generator learns to make data that is indistinguishable from real data to the discriminator. The general structure of a GAN is shown in the diagram above, using MNIST images as data. The latent sample is a random vector that the generator uses to construct its fake images. This is often called a latent vector and that vector space is called latent space. As the generator trains, it figures out how to map latent vectors to recognizable images that can fool the discriminator. If you're interested in generating only new images, you can throw out the discriminator after training. In this notebook, I'll show you how to define and train these adversarial networks in PyTorch and generate new images!

projects

pytorch-gan

PyTorch implementations of Generative Adversarial Networks.

takehomedatachallenges

My solution to the book <A collection of Data Science Take-home Challenges>