Neural Networks (ANN) using Keras and TensorFlow in Python

本课程涵盖什么内容？
本课程将教您创建基于神经网络的模型（即深度学习模型）以解决业务问题的所有步骤。
以下是关于 ANN 的本课程的课程内容：
第 1 部分 – Python 基础
这部分让您开始使用 Python。
这部分将帮助您在您的系统上设置 python 和 Jupyter 环境，它将教您如何在 Python 中执行一些基本操作。我们将了解不同库的重要性，例如 Numpy、Pandas & 海伯恩。
第 2 部分 – 理论概念
这部分将使您对神经网络中涉及的概念有一个深入的了解。
在本节中，您将学习有关单个单元或感知器以及如何堆叠感知器以创建网络体系结构的信息。设置架构后，我们了解梯度下降算法以找到函数的最小值，并了解如何使用它来优化我们的网络模型。
第 3 部分 – 在 Python 中创建回归和分类 ANN 模型
在这一部分中，您将学习如何在 Python 中创建 ANN 模型。
我们将通过使用 Sequential API 创建 ANN 模型来解决分类问题来开始本节。我们学习如何定义网络架构、配置模型和训练模型。然后我们评估我们训练模型的性能并使用它来预测新数据。我们还解决了回归问题，在该问题中我们试图预测某个位置的房价。我们还将介绍如何使用函数式 API 创建复杂的 ANN 架构。最后，我们学习如何保存和还原模型。
在这一部分，我们也了解了 Keras 和 TensorFlow 等库的重要性。
第 4 部分 – 数据预处理
在这一部分中，您将了解需要采取哪些操作来为分析准备数据，这些步骤对于创建有意义的数据非常重要。
在本节中，我们将从决策树的基本理论开始，然后介绍数据预处理主题，例如  缺失值插补、变量转换和测试训练拆分。
第 5 部分 – 经典机器学习技术 – 线性回归\n本节从简单线性回归开始，然后介绍多元线性回归。
我们已经涵盖了每个概念背后的基本理论，但没有对其进行过多的数学处理，以便您
了解概念的来源以及它的重要性。但即使你不明白
， 只要您按照实践讲座中的方法学习并运行并解释结果，就可以了。

$199.99 | Created by Start-Tech Academy | Last updated 3/2020
Duration: 9 hours | Video: h264, 1280×720 | Audio: AAC, 44 KHz, 2 Ch | 3 GB
Genre: eLearning | Language: English + Sub | 62 lectures

What you’ll learn

Get a solid understanding of Artificial Neural Networks (ANN) and Deep Learning

Understand the business scenarios where Artificial Neural Networks (ANN) is applicable

Building a Artificial Neural Networks (ANN) in Python

Use Artificial Neural Networks (ANN) to make predictions

Learn usage of Keras and Tensorflow libraries

Use Pandas DataFrames to manipulate data and make statistical computations.

Requirements

Students will need to install Python and Anaconda software but we have a separate lecture to help you install the sameStudents will need to install Python and Anaconda software but we have a separate lecture to help you install the same

Description

You’re looking for a complete Artificial Neural Network (ANN) course that teaches you everything you need to create a Neural Network model in Python, 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 using Keras and Tensorflow libraries and analyze their results.
Confidently practice, discuss and understand Deep Learning concepts
How 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 – Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy
Our Promise
Teaching 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 Assignments
With 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 basics
This 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 Concepts
This 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
In 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 Preprocessing
In 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.
Part 5 – Classic ML technique – Linear Regression\nThis section starts with simple linear regression and then covers multiple linear regression.
We have covered the basic theory behind each concept without getting too mathematical about it so that you
understand where the concept is coming from and how it is important. But even if you don’t understand
it,  it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.
We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results and how do we finally interpret the result to find out the answer to a business problem.
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.
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Go ahead and click the enroll button, and I’ll see you in lesson 1!
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Cheers
Start-Tech Academy
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Below are some popular FAQs of students who want to start their Deep learning journey-
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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.

Who this course is for:

People pursuing a career in data science
Working Professionals beginning their Neural Network journey
Statisticians needing more practical experience
Anyone curious to master ANN from Beginner level in short span of time