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所在平台: Udemy |
课程主页: https://www.udemy.com/course/the-complete-recurrent-neural-network-with-python-course/
课程评论：没有评论
课程名称：完整的循环神经网络与Python课程 课程概述：对机器学习、深度学习和人工智能感兴趣吗？那么这个课程非常适合你！该课程由一位软件工程师设计，基于多年的实践经验，旨在以简单易懂的方式分享复杂的理论、算法和编码库。我将引导你逐步进入深度学习领域，通过每个教程，你将获得新技能并加深对这一极具挑战性但又颇具前景的数据科学子领域的理解。 本课程内容丰富且富有趣味，同时深入探讨循环神经网络（RNN）的相关知识。课程的全新版本涵盖了大量的工具和技术，包括：深度学习、Google Colab、Keras、Matplotlib、数据集的训练和测试集分割、神经网络训练、模型构建、结果分析、模型编译、预测、准确性测试、混淆矩阵、ROC曲线、文本分析、图像分析、嵌入层、词嵌入、长短期记忆（LSTM）模型、序列到向量模型、向量到序列模型、双向LSTM、序列到序列模型、将词转化为特征向量、频率-逆文档频率（TF-IDF）、文本数据清理、文档处理成词元、潜在狄利克雷分配（LDA）主题建模、文档分解、自动编码器、Numpy、Pandas、Tensorflow、情感分析、离线学习等。 此外，课程包含许多基于现实生活示例的实践练习，使你不仅能够学习理论，还可以动手实践，构建自己的模型。课程中还包含若干项目供你练习和巩固知识，项目内容包括：比特币预测、股票价格预测、电影评论情感分析、IMDB项目和MNIST项目等。
Interested in the field of Machine Learning, Deep Learning, and Artificial Intelligence? Then this course is for you!This course has been designed by a software engineer. I hope with the experience and knowledge I did gain throughout the years, I can share my knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way.I will walk you step-by-step into Deep Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.This course is fun and exciting, but at the same time, we dive deep into Recurrent Neural Network. Throughout the brand new version of the course, we cover tons of tools and technologies including:Deep Learning.Google ColabKeras.Matplotlib.Splitting Data into Training Set and Test Set. Training Neural Network.Model building.Analyzing Results.Model compilation.Make a Prediction.Testing Accuracy.Confusion Matrix.ROC Curve.Text analysis.Image analysis.Embedding layers.Word embedding.Long short-term memory (LSTM) models.Sequence-to-vector models.Vector-to-sequence models.Bi-directional LSTM.Sequence-to-sequence models.Transforming words into feature vectors.frequency-inverse document frequency.Cleaning text data.Processing documents into tokens.Topic modelling with latent Dirichlet allocationDecomposing text documents with LDA.Autoencoder.Numpy.Pandas.Tensorflow.Sentiment Analysis.Matplotlib.out-of-core learning.Bi-directional LSTM.Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. There are several projects for you to practice and build up your knowledge. These projects are listed below:Bitcoin PredictionStock Price PredictionMovie Review sentimentIMDB Project.MNIST Project.
