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所在平台: Udemy |
课程主页: https://www.udemy.com/course/learn-artificial-neural-network-from-scratch-in-python/
课程评论：没有评论
**课程名称：** 从零开始使用Python学习人工智能神经网络 **课程概述：** 本课程旨在帮助学习者从零开始，使用Python构建和理解人工智能神经网络（ANN）。课程将深入讲解构成神经网络的核心概念，如梯度下降、前向传播和反向传播，并教授如何使用Python（包括NumPy库）从头开始实现这些算法。此外，课程还会介绍如何使用Scikit-learn等流行库来创建和优化神经网络模型。 **学习目标：** * 识别可使用神经网络模型解决的业务问题。 * 深入理解梯度下降、前向传播和反向传播等高级神经网络概念。 * 能够使用Python创建神经网络模型，并掌握超参数调优以优化模型。 * 充满信心地实践、讨论和理解深度学习概念。 * 构建第一个真正的人工神经网络。 **课程内容：** * **第一部分：Python基础** * Python环境搭建（Jupyter Notebook）。 * Python基础知识复习，包括数据结构、推导式、面向对象编程等。 * NumPy, Pandas, Seaborn, Matplotlib等关键Python库的介绍和基本操作。 * **第二部分：理论概念** * 神经元及其在神经网络中的作用。 * 神经网络架构的设计。 * 梯度下降算法，用于寻找函数最小值及优化模型。 * **第三部分：使用Python和R创建回归与分类ANN模型** * 从零开始在Python中构建神经网络模型。 * 使用Scikit-learn等库创建神经网络模型。 * （提及R，但主要侧重Python实现） * **第四部分：反向传播的数值示例教程** * 通过具体的数值例子，深入理解反向传播的原理。 * 运用偏导数更新反向传播中的权重，结合梯度下降算法。 **适合人群：** * 希望深入学习深度学习的初学者。 * 对机器学习和数据科学感兴趣的学习者。 * 想要超越线性回归、逻辑回归等基础模型，学习自动特征学习的学习者。 **课程产出：** 完成课程后，学习者将能够自信地使用Python创建神经网络模型，并对如何利用ANN解决业务问题有透彻的理解。
Welcome to the course where we will learn about Artificial Neural Network (ANN) From Scratch!If you're looking for a complete Course on Deep Learning using ANN that teaches you everything you need to create a Neural Network model in Python?You've found the right Neural Network course!After completing this course you will be able to:Identify the business problem which can be solved using Neural network Models.Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc.Create Neural network models in Python and ability to optimize the model tuning hyper parametersConfidently practice, discuss and understand Deep Learning conceptsThis course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features.What is covered in this course?This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.Below are the course contents of this course on ANN:Part 1 - Python basics This part gets you started with Python and learn the brush up the basics like data structures, comprehensions, Object Oriented Programming and so on.This part will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas, Seaborn and matplotlib libraries.Part 2 - Theoretical ConceptsThis part will give you a solid understanding of concepts involved in Neural Networks.In this section you will learn about the neurons and how neurons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.Part 3 - Creating Regression and Classification ANN model in Python and RIn this part you will learn how to create ANN models in Python.We will learn how to model the neural network in two ways: first we model it from scratch and after that using scikit-learn library.Part 4 - Tutorial numerical examples on BackpropagationOne of the most important concept of ANN is backpropagation, so in order to apply the theory we learnt in lecture session in the real world neural networks, we are going to execute backpropagation taking one numerical example. We are going to take the help of partial differentiation and update the weights in backpropagation using gradient descent algorithms. By the end of this course, your confidence in creating a Neural Network model in Python will soar. You'll have a thorough understanding of how to use ANN to create predictive models and solve business problems.
