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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ahol-dl4nlp1/
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课程名称：深度学习与自然语言处理 - 第一部分 课程概述：本课程是“深度学习与自然语言处理”系列的一部分，将介绍深度学习的基础概念，如多层感知器、词嵌入和递归神经网络。这些概念为理解高级的自然语言处理深度学习模型奠定了基础。课程分为三个部分。 第一部分：讨论人工神经网络的基本概念，包括激活函数（如线性、阶跃、sigmoid、tanh、relu、leaky relu）、积分函数、感知器和反向传播算法。讲解深度学习的概念，以及它与机器学习和人工智能的关系。还包括处理神经网络训练中的过拟合的方法，如正则化、提前停止和 dropout。 第二部分：介绍各种词嵌入方法。从基本的方法开始，如独热编码和奇异值分解（SVD）。接着讨论流行的word2vec模型，包括CBOW和Skipgram方法。讲解提高softmax计算效率的多种方法，以及GloVe。特别地，涉及跨语言嵌入和常用于变换器模型的子词嵌入方法，如BPE（字节对编码）、wordPiece和SentencePiece。 第三部分：以ngram模型的总体讨论开始，简要介绍神经网络语言模型（NNLM）。详细理解递归神经网络（RNN）的工作原理，讨论RNN的变种，如双向RNN和深度双向RNN。此外，讲解梯度消失和梯度爆炸问题，并详细介绍LSTM和GRU的架构。
This course is a part of "Deep Learning for NLP" Series. In this course, I will introduce basic deep learning concepts like multi-layered perceptrons, word embeddings and recurrent neural networks. These concepts form the base for good understanding of advanced deep learning models for Natural Language Processing.The course consists of three sections. In the first section, I will talk about Basic concepts in artificial neural networks like activation functions (like ramp, step, sigmoid, tanh, relu, leaky relu), integration functions, perceptron and back-propagation algorithms. I also talk about what is deep learning, how is it related to machine learning and artificial intelligence? Finally, I will talk about how to handle overfittting in neural network training using methods like regularization, early stopping and dropouts.In the second section, I will talk about various kinds of word embedding methods. I will start with basic methods like Onehot encoding and Singular Value Decomposition (SVD). Next I will talk about the popular word2vec model including both the CBOW and Skipgram methods. Further, I will talk about multiple methods to make the softmax computation efficient. This will be followed by discussion on GloVe. As special word embedding topics I will cover Cross-lingual embeddings. Finally, I will also talk about sub-word embeddings like BPE (Byte Pair Encoding), wordPiece, SentencePiece which are popularly used for Transformer based models.In the third session, I will start with general discussion on ngram models. Next I will briefly introduce the neural network language model (NNLM). Then we will spend quite some time understanding how RNNs work. We will also talk about RNN variants like BiRNNs, Deep BiRNNs. Then I will discuss the vanishing and exploding gradients problem. This will be followed by details of the LSTMs and GRUs architectures.
