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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-convolutional-neural-networks-theano-tensorflow/
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课程名称：深度学习：Python中的卷积神经网络 课程概述：您是否曾想过像OpenAI的ChatGPT、GPT-4、DALL-E、Midjourney和Stable Diffusion等人工智能技术是如何运作的？在本课程中，您将学习这些开创性应用的基础知识。课程将深入探讨强大的深度学习架构之一——卷积神经网络（CNN），该架构在计算机视觉任务中，如目标检测、图像分割以及生成不存在于现实世界中的逼真图像等，取得了最先进的成果。 本课程将教授您卷积的基本原理及其在深度学习甚至自然语言处理（NLP）中的应用，涵盖现代技术如数据增强和批量标准化。您将亲自构建现代架构，例如VGG。 课程内容包括： - 机器学习和神经元的基础知识（复习以便热身！） - 分类和回归的神经网络（复习以便热身！） - 如何在代码中建模图像数据 - 如何为NLP建模文本数据（包括文本的预处理步骤） - 如何使用TensorFlow 2构建CNN - 如何在TensorFlow 2中使用批量标准化和 dropout 正规化 - 如何在TensorFlow 2中进行图像分类 - 如何为您自己的自定义图像数据集进行数据预处理 - 如何在TensorFlow 2中使用嵌入进行NLP - 如何构建用于NLP的文本分类CNN（实例：垃圾邮件检测、情感分析、词性标注、命名实体识别） 所有课程所需的材料均可免费下载和安装。我们将主要使用Numpy、Matplotlib和TensorFlow进行工作。讲师始终可以回答您的问题，并协助您在数据科学之旅中不断进步。 本课程专注于“如何构建和理解”，而不仅仅是“如何使用”。任何人都可以在15分钟内学习使用API，而不需要深入学习。课程强调通过实验来“亲眼所见”，让学生了解模型内部发生的事情。如果您想了解比机器学习模型更深层次的内容，那么本课程将非常适合您。 建议的先修知识： - 矩阵加法和乘法 - 基本概率（条件和联合分布） - Python编程：if/else、循环、列表、字典、集合 - Numpy编程：矩阵和向量运算，加载CSV文件 推荐的学习顺序请查看“机器学习和AI先修课程路线图”讲座（可在任何课程的FAQ中找到，包括免费的Numpy课程）。 独特特点： - 每行代码详细解释，若有异议请随时联系讲师 - 不浪费时间在键盘上“输入”代码——诚实地说，没人能在20分钟内从零开始写出有价值的代码 - 不惧怕大学水平的数学，提供其他课程未提及的重要算法细节
Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.Learn about one of the most powerful Deep Learning architectures yet!The Convolutional Neural Network (CNN) has been used to obtain state-of-the-art results in computer vision tasks such as object detection, image segmentation, and generating photo-realistic images of people and things that don't exist in the real world!This course will teach you the fundamentals of convolution and why it's useful for deep learning and even NLP (natural language processing).You will learn about modern techniques such as data augmentation and batch normalization, and build modern architectures such as VGG yourself.This course will teach you:The basics of machine learning and neurons (just a review to get you warmed up!)Neural networks for classification and regression (just a review to get you warmed up!)How to model image data in codeHow to model text data for NLP (including preprocessing steps for text)How to build an CNN using Tensorflow 2How to use batch normalization and dropout regularization in Tensorflow 2How to do image classification in Tensorflow 2How to do data preprocessing for your own custom image datasetHow to use Embeddings in Tensorflow 2 for NLPHow to build a Text Classification CNN for NLP (examples: spam detection, sentiment analysis, parts-of-speech tagging, named entity recognition)All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Tensorflow. I am always available to answer your questions and help you along your data science journey.This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.Suggested Prerequisites:matrix addition and multiplicationbasic probability (conditional and joint distributions)Python coding: if/else, loops, lists, dicts, setsNumpy coding: matrix and vector operations, loading a CSV fileWHAT ORDER SHOULD I TAKE YOUR COURSES IN?:Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)UNIQUE FEATURESEvery line of code explained in detail - email me any time if you disagreeNo wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratchNot afraid of university-level math - get important details about algorithms that other courses leave out
