Learning Goals Understand the convolution operation Understand the pooling operation Remember the vocabulary used in convolutional neural network (padding, stride, filter, ...) Build a convolutional neural network for image multi-class classification…
Learning Goals Understand multiple foundational papers of convolutional neural networks Analyze the dimensionality reduction of a volume in a very deep network Understand and Implement a Residual network Build a deep neural network using Keras Implem…
Learning Goals: Understand the challenges of Object Localization, Object Detection and Landmark Finding Understand and implement non-max suppression Understand and implement intersection over union Understand how we label a dataset for an object dete…
Building your Recurrent Neural Network - Step by Step Welcome to Course 5's first assignment! In this assignment, you will implement your first Recurrent Neural Network in numpy. Recurrent Neural Networks (RNN) are very effective for Natural Language…
Improvise a Jazz Solo with an LSTM Network Welcome to your final programming assignment of this week! In this notebook, you will implement a model that uses an LSTM to generate music. You will even be able to listen to your own music at the end of th…
Character level language model - Dinosaurus land Welcome to Dinosaurus Island! 65 million years ago, dinosaurs existed, and in this assignment they are back. You are in charge of a special task. Leading biology researchers are creating new breeds of…
[解释] It is appropriate when every input should be matched to an output. [解释] in a language model we try to predict the next step based on the knowledge of all prior steps. [解释] Γu is a vector of dimension equal to the number of hidden units in the LS…
Learning Goals Understand why Machine Learning strategy is important Apply satisficing and optimizing metrics to set up your goal for ML projects Choose a correct train/dev/test split of your dataset Understand how to define human-level performance U…
Learning Goals Understand what multi-task learning and transfer learning are Recognize bias, variance and data-mismatch by looking at the performances of your algorithm on train/dev/test sets [中文翻译] 学习目标 了解什么是多任务学习和迁移学习 通过在训练/开发/测试集上查看算法的性能, 识别偏差.方差和…
第一周 循环序列模型(Recurrent Neural Networks) 1.1 为什么选择序列模型?(Why Sequence Models?) 1.2 数学符号(Notation) 这个输入数据是 9 个单词组成的序列,所以会有 9 个特征集和来表示这 9 个 单词,并按序列中的位置进行索引,用\(…