首页 Python 正文

实用神经网络与Python深度学习教程

2021.11.18 Python 4

实用神经网络与Python深度学习教程 Python-第1张
Practical Neural Networks and Deep Learning in Python
实现PyTorch、Keras、Tensorflow算法的完整指南:Python中的神经网络和深度学习

你会学到:
将Anaconda/iPython的力量用于实用数据科学(包括人工智能应用)
了解如何在Anaconda中安装和使用重要的深度学习包(包括Keras、H20、Tensorflow和PyTorch)
用张量流实现统计和机器学习技术
使用深度学习包(包括Keras)实现神经网络建模

MP4 |视频:h264,1280×720 |音频:AAC,44.1 KHz,2 Ch
语言:英语+中英文字幕(根据原英文字幕机译更准确) |时长:84节课(8小时38分钟)|大小解压后:4.4 GB

要求
能够在您的计算机/笔记本电脑上安装蟒蛇环境
知道如何在Anaconda中安装和加载包
学习处理图像数据的兴趣
遵循代码需要Python编程语法和概念的基本知识(例如函数和编程流程)
之前接触过Python数据科学概念会很有用

描述
这是一个完整的神经网络和深度学习训练与PYTORCH,H2O,KERAS & TENSORFLOW在PYTHON!

这是一个完整的5小时+深度学习训练营,将帮助您使用最重要的Python深度学习框架之一——PyTorch、H2O、Keras & Tensorflow来学习基本的机器学习、神经网络和深度学习。
实用神经网络与Python深度学习教程 Python-第2张
实用神经网络与Python深度学习教程 Python-第3张
以下是您应该参加本课程的原因:

本课程是您使用Python中的PyTorch、H2O、Keras和Tensorflow框架进行实用机器和深度学习的完整指南。

这意味着,本课程涵盖了这些体系结构的重要方面,如果您选修了本课程,您可以不再选修其他课程或购买关于不同的基于Python的深度学习体系结构的书籍。

在这个大数据时代,全球各地的公司都使用Python来筛选他们可以处理的信息,PyTorch、Keras、H2o、Tensorflow等框架的出现正在彻底改变深度学习…

通过熟练掌握PyTorch、H2O、Keras和Tensorflow,你可以给你的公司带来竞争优势,并推动你的职业生涯更上一层楼。
实用神经网络与Python深度学习教程 Python-第4张

这是我对你的承诺:完成这一门课程&成为基于PYTHON的实用数据科学专家!

但首先。我叫密涅瓦·辛格,毕业于牛津大学地理与环境专业。我最近在剑桥大学完成了博士学位(热带生态和保护)。

我在使用数据科学相关技术分析来自不同来源的真实数据以及为国际同行评审期刊制作出版物方面有几年的经验。

在我的研究过程中,我意识到几乎所有的Python数据科学课程和书籍都没有说明这个主题的多维性,而是将数据科学与机器学习交替使用。

这使学生对这门学科的知识不全面。另一方面,我的课程将在PyTorch、H2O、Tensorflow和Keras框架内为您提供数据科学所有方面的坚实基础。

与其他Python课程和书籍不同的是,你将真正学会在真实数据上使用PyTorch、H20、Tensorflow和Keras!我遇到的大多数其他资源展示了如何在使用有限的内置数据集上使用PyTorch。

发现7个完整的部分,涉及重要深度学习框架的各个方面:

Python数据科学的完整介绍和强大的Python驱动的数据科学框架,Anaconda
用Python实现数据科学技术的Jupyter笔记本入门
关于PyTorch、H2o、Tensorflow和Keras安装的全面介绍,以及对其他Python数据科学包的简要介绍
熊猫和Numpy等重要数据科学包的工作简介
PyTorch、H2o、Tensorflow和Keras语法的基础知识
使用Python处理图像数据的基础知识
人工神经网络、深度神经网络和卷积神经网络等神经网络概念背后的理论(美国有线电视新闻网)
您甚至会发现如何使用PyTorch、Keras和Tensorflow(在真实数据上)创建人工神经网络和深度学习结构

但是,等等!这不仅仅是任何其他数据科学课程:

您将从吸收最有价值的PyTorch、Tensorflow和Keras基础知识和技术开始。

我使用易于理解的实践方法来简化和解决即使是最困难的概念。

我的课程将帮助你使用从不同来源获得的真实数据来实现这些方法。许多课程使用虚构的数据,这并不能让学生在现实生活中实现基于Python的数据科学。
实用神经网络与Python深度学习教程 Python-第5张
学完本课程后,您将很容易地使用像Numpy、Pandas和PIL这样的包来处理Python中的真实数据,并在最重要的深度学习架构中获得流畅度。我甚至会给大家介绍深度学习模型,比如卷积神经网络(CNN)!!

该课程的潜在动机是确保您今天可以将基于Python的数据科学应用于实际数据,开始为您自己的项目分析数据,无论您的技能水平如何,并以您的数据科学能力的实际例子打动您的潜在雇主。

这是一门实践性很强的课程,也就是说,我们将花一些时间来处理一些与数据科学相关的理论概念。然而,本课程的大部分将侧重于在真实数据上实现不同的技术并解释结果。我们将解决的一些问题包括识别信用卡欺诈和对不同水果的图像进行分类。

在每一个视频之后,你会学到一个新的概念或技术,你可以应用到你自己的项目中!

立即加入课程!

这门课是给谁的
对将Anaconda环境用于Python数据科学应用程序感兴趣的学生
对Keras、Tensorflow、PyTorch环境感兴趣的学生
学生有兴趣学习神经网络技术背后的基本理论概念,如卷积神经网络
在真实数据上实现人工神经网络
实现深度神经网络
在图像数据上实现卷积神经网络
使用真实图像数据构建图像分类器并评估其性能
实用神经网络与Python深度学习教程 Python-第6张
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 84 lectures (8h 38m) | Size: 4.06 GB
Your Complete Guide to Implementing PyTorch, Keras, Tensorflow Algorithms: Neural Networks and Deep Learning in Python

What you’ll learn:
Harness The Power Of Anaconda/iPython For Practical Data Science (Including AI Applications)
Learn How To Install & Use Important Deep Learning Packages Within Anaconda (Including Keras, H20, Tensorflow and PyTorch)
Implement Statistical & Machine Learning Techniques With Tensorflow
Implement Neural Network Modelling With Deep learning Packages Including Keras

Requirements
The Ability To Install the Anaconda Environment On Your Computer/Laptop
Know how to install and load packages in Anaconda
Interest in Learning to Process Image Data
Basic Knowledge of Python Programming Syntax and Concepts is Needed to Follow the Code (e.g. functions and programming flows)
Prior Exposure to Python Data Science Concepts Will be Useful

Description
THIS IS A COMPLETE NEURAL NETWORKS & DEEP LEARNING TRAINING WITH PYTORCH, H2O, KERAS & TENSORFLOW IN PYTHON!

It is a full 5-Hour+ Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning using one of the most important Python Deep Learning frameworks- PyTorch, H2O, Keras & Tensorflow.

HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:

This course is your complete guide to practical machine & deep learning using the PyTorch, H2O, Keras and Tensorflow framework in Python.

This means, this course covers the important aspects of these architectures and if you take this course, you can do away with taking other courses or buying books on the different Python-based- deep learning architectures.

In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of frameworks such as PyTorch, Keras, H2o, Tensorflow is revolutionizing Deep Learning…

By gaining proficiency in PyTorch, H2O, Keras and Tensorflow, you can give your company a competitive edge and boost your career to the next level.

THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL PYTHON BASED DATA SCIENCE!

But first things first. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment), graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).

I have several years of experience in analyzing real-life data from different sources using data science-related techniques and producing publications for international peer-reviewed journals.

Over the course of my research, I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning.

This gives students an incomplete knowledge of the subject. My course, on the other hand, will give you a robust grounding in all aspects of data science within the PyTorch, H2O, Tensorflow and Keras framework.

Unlike other Python courses and books, you will actually learn to use PyTorch, H20, Tensorflow and Keras on real data! Most of the other resources I encountered showed how to use PyTorch on in-built datasets which have limited use.

DISCOVER 7 COMPLETE SECTIONS ADDRESSING EVERY ASPECT OF IMPORTANT DEEP LEARNING FRAMEWORKS:

• A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda
• Getting started with Jupyter notebooks for implementing data science techniques in Python
• A comprehensive presentation about PyTorch, H2o, Tensorflow and Keras installation and a brief introduction to the other Python data science packages
• A brief introduction to the working of important data science packages such as Pandas and Numpy
• The basics of the PyTorch, H2o, Tensorflow and Keras syntax
• The basics of working with imagery data in Python
• The theory behind neural network concepts such as artificial neural networks, deep neural networks and convolutional neural networks (CNN)
• You’ll even discover how to create artificial neural networks and deep learning structures with PyTorch, Keras and Tensorflow (on real data)

BUT, WAIT! THIS ISN’T JUST ANY OTHER DATA SCIENCE COURSE:

You’ll start by absorbing the most valuable PyTorch, Tensorflow and Keras basics and techniques.

I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts.

My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python-based data science in real -life.

After taking this course, you’ll easily use packages like Numpy, Pandas, and PIL to work with real data in Python along with gaining fluency in the most important of deep learning architectures. I will even introduce you to deep learning models such as Convolution Neural network (CNN) !!

The underlying motivation for the course is to ensure you can apply Python-based data science on real data into practice today, start analyzing data for your own projects whatever your skill level, and impress your potential employers with actual examples of your data science abilities.

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, the majority of the course will focus on implementing different techniques on real data and interpret the results. Some of the problems we will solve include identifying credit card fraud and classifying the images of different fruits.

After each video, you will learn a new concept or technique which you may apply to your own projects!

JOIN THE COURSE NOW!

Who this course is for
Students interested in using the Anaconda environment for Python data science applications
Students interested in getting started with the Keras, Tensorflow,PyTorch environment
Students Interested in Learning the Basic Theoretical Concepts behind Neural Networks techniques Such as Convolutional neural network
Implement ANN on Real Data
Implement Deep Neural Networks
Implement Convolutional Neural Networks (CNN) on Imagery data
Build Image Classifiers Using Real Imagery Data and Evaluate Their Performance

隐藏内容: ********, 支付¥10下载

下载说明:
1、电脑端:浏览器打开网页,扫码打赏后自动显示百度网盘链接,如无显示请刷新网页。
2、手机端:需微信内打开素材网页,打赏后返回原素材页面即可自动显示网盘链接。
3、资源默认为百度网盘链接,如链接失效或无法获取 请联系客服微信 yunqiaonet 解决
4、本站持续更新国内外CG教程软件素材等资源,打开会员平台 yunqiaowang.cn 登录充值38元成为会员免费获取更多站内资源。
【实用神经网络与Python深度学习教程】