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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-with-keras-and-tensorflow-in-python-and-r/
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课程名称：使用Keras的人工神经网络（ANN）教程（Python和R） 课程概述：您是否在寻找一门关于深度学习的完整课程，使用Keras和Tensorflow教授您如何在Python和R中创建神经网络模型？您找到了正确的神经网络课程！通过完成本课程，您将能够识别可以通过神经网络模型解决的商业问题，深入了解梯度下降、前向传播和反向传播等高级神经网络概念，并使用Keras和Tensorflow库在Python和R中创建神经网络模型并分析其结果。同时，您将能够自信地练习、讨论和理解深度学习的概念。 课程帮助：完成课程后，所有学员将获得可验证的结业证书。如果您是业务分析师、管理人员或希望在现实商业问题中应用深度学习的学生，本课程将为您打下坚实的基础，教授您一些最先进的神经网络概念及其Python实现，而无需过多数学理论。 选择本课程的理由：本课程涵盖创建预测模型所需的所有步骤。大多数课程只关注如何进行分析，而我们认为，深入的理论理解使我们能够创建良好的模型，并在分析完后，评估模型的优劣及解释结果，从而真正帮助商业。 授课资格：本课程由Abhishek和Pukhraj主讲，他们在全球分析咨询公司担任经理，帮助企业解决业务问题，并将实践经验融入课程中。他们也是一些最受欢迎的在线课程的创作者，拥有超过250,000的注册和成千上万的五颗星评价。 课程内容： - 第一部分：Python和R基础 - 学习Python基础，设置Python和Jupyter环境，掌握基本操作，理解Numpy、Pandas和Seaborn等库的重要性。 - 第二部分：理论概念 - 了解神经网络的基本概念，学习感知器和网络架构，掌握梯度下降算法的工作原理及其在模型优化中的应用。 - 第三部分：在Python和R中创建回归和分类的ANN模型 - 使用Keras创建ANN模型，定义网络架构、配置和训练模型，评估模型性能，对新数据进行预测，及解决房价预测的回归问题。同时学习复杂ANN架构的创建和模型的保存与恢复。 - 第四部分：数据预处理 - 学习准备数据进行分析的必要步骤，包括决策树基础理论、缺失值填补、变量变化和训练测试集划分。 通过本课程，您将深刻理解如何使用ANN创建预测模型并解决商业问题，信心满满地使用Python实现此过程。点击注册按钮，我们在第一课见！ 如果您有任何问题，可以随时在课程中提问或直接联系讲师，课程中包含课堂笔记、练习测试和最终实践作业，以帮助您更好地掌握学习内容。
You're looking for a complete Course on Deep Learning using Keras and Tensorflow that teaches you everything you need to create a Neural Network model in Python and R, right?You've found the right Neural Networks 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 R using Keras and Tensorflow libraries and analyze their results.Confidently practice, discuss and understand Deep Learning conceptsHow this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course.If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in Python without getting too Mathematical.Why should you choose this course?This course covers all the steps that one should take to create a predictive model using Neural Networks.Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model. And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 250,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Practice test, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning. 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 and R basicsThis part gets you started with Python.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.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 single cells or Perceptrons and how Perceptrons 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 start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.We also understand the importance of libraries such as Keras and TensorFlow in this part.Part 4 - Data PreprocessingIn this part you will learn what actions you need to take to prepare Data for the analysis, these steps are very important for creating a meaningful.In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like missing value imputation, variable transformation and Test-Train split. 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.Go ahead and click the enroll button, and I'll see you in lesson 1!CheersStart-Tech Academy------Below are some popular FAQs of students who want to start their Deep learning journey-Why use Python for Deep Learning?Understanding Python is one of the valuable skills needed for a career in Deep Learning.Though it hasn't always been, Python is the programming language of choice for data science. Here's a brief history: In 2016, it overtook R on Kaggle, the premier platform for data science competitions. In 2017, it overtook R on KDNuggets's annual poll of data scientists' most used tools. In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it's nice to know that employment opportunities are abundant (and growing) as well.What is the difference between Data Mining, Machine Learning, and Deep Learning?Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge-and further automatically applies that information to data, decision-making, and actions.Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.
