本文展示 3 種可以讓你馬上運用 CartoonGAN 來生成動漫的方法。其中包含了我們的 Github 專案、TensorFlow.js 應用以及一個事先為你準備好的 Colab 筆記本。有興趣的同學還可學習如何利用 TensorFlow 2.0 來訓練自己的專屬 CartoonGAN。
這篇文章展示一個由 TensorFlow 2.0 以及 TensorFlow.js 實現的文本生成應用。本文也會透過深度學習專案常見的 7 個步驟，帶領讀者一步步了解如何實現一個這樣的應用。閱讀完本文，你將對開發 AI 應用的流程有些基礎的了解。
Naive Word2vec implementation using Tensorflow
The goal here is to practice building convolutional neural networks to classify notMNIST characters using TensorFlow. As image size become bigger and bigger, it become unpractical to train fully-connected NN because there will be just too many parameters and thus the model will overfit very soon. And CNN solve this problem by weight sharing. We will start by building a CNN with two convolutional layers connected by a fully connected layer and then try also pooling layer and other thing to improve the model performance.
The goal of this assignment is to explore regularization techniques.
The goal here is to progressively train deeper and more accurate models using TensorFlow. We will first load the notMNIST dataset which we have done data cleaning. For the classification problem, we will first train two logistic regression models use simple gradient descent, stochastic gradient descent (SGD) respectively for optimization to see the difference between these optimizers.