When will I receive my Course Certificate?

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

Why can’t I audit this course?

This course is currently available only to learners who have paid or received financial aid, when available.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Deep Neural Network for Beginners Using Python

Deep Neural Network for Beginners Using Python

位教师：Packt - Course Instructors

包含在 Coursera Plus 中

了解更多

5个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

9 小时完成

灵活的计划

自行安排学习进度

5个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

9 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Understand the basics of training a DNN using the Gradient Descent algorithm.
Apply knowledge to implement a complete DNN using NumPy.
Analyze and create a complete structure for DNN from scratch using Python.
Evaluate and work on a project using deep learning for the IRIS dataset.

您将获得的技能

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

4 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有5个模块

Updated in May 2025.

This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Are you ready to become a deep learning expert? This step-by-step course guides you from basic to advanced levels in deep learning using Python, the hottest language for machine learning. Each tutorial builds on previous knowledge and assigns tasks solved in the next video. You will: - Learn to train machines to predict like humans by mastering data preprocessing, general machine learning concepts, and deep neural networks (DNNs). - Cover the architecture of neural networks, the Gradient Descent algorithm, and implementing DNNs using NumPy and Python. - Understand DNN methodologies with real-world datasets, such as the IRIS dataset. Designed for those interested in data science or advancing their skills in DNNs, this course requires a background in deep learning and a basic understanding of Python and mathematics will be helpful. It’s clear and beginner-friendly, teaching theoretical concepts followed by practical implementation.

In this module, we will provide a brief overview of the course and introduce the instructor. We will also outline the learning objectives and what students can expect to achieve by the end of the course.

涵盖的内容

3个视频1篇阅读材料

In this module, we will delve into the foundational aspects of deep learning. We will start by examining a real-world problem and progressively introduce key concepts such as perceptrons, linear equations, and error functions. This section includes hands-on coding exercises to solidify understanding.

涵盖的内容

37个视频1个插件

37个视频总计126分钟

Problem to Solve Part 12分钟
Problem to Solve Part 22分钟
Problem to Solve Part 31分钟
Linear Equation3分钟
Linear Equation Vectorized3分钟
3D Feature Space3分钟
N-Dimensional Space2分钟
Theory of Perceptron1分钟
Implementing Basic Perceptron5分钟
Logical Gates for Perceptrons2分钟
Perceptron Training Part 11分钟
Perceptron Training Part 23分钟
Learning Rate3分钟
Perceptron Training Part 33分钟
Perceptron Algorithm1分钟
Coding Perceptron Algo (Data Reading and Visualization)5分钟
Coding Perceptron Algo (Perceptron Step)7分钟
Coding Perceptron Algo (Training Perceptron)6分钟
Coding Perceptron Algo (Visualizing the Results)4分钟
Problem with Linear Solutions2分钟
Solution to Problem1分钟
Error Functions2分钟
Discrete Versus Continuous Error Function2分钟
Sigmoid Function3分钟
Multi-Class Problem1分钟
Problem of Negative Scores3分钟
Need of SoftMax1分钟
Coding SoftMax4分钟
One-Hot Encoding2分钟
Maximum Likelihood Part 15分钟
Maximum Likelihood Part 23分钟
Cross Entropy4分钟
Cross Entropy Formulation7分钟
Multi-Class Cross Entropy3分钟
Cross Entropy Implementation4分钟
Sigmoid Function Implementation1分钟
Output Function Implementation2分钟

1个插件总计15分钟

Implementing Logic Gates with Perceptrons15分钟

In this module, we will focus on more advanced topics in deep learning. We will cover gradient descent, logistic regression, and the architecture of neural networks. Practical coding sessions will help learners apply these concepts and build their own deep learning models.

涵盖的内容

31个视频1个作业1个插件

31个视频总计153分钟

Introduction to Gradient Descent5分钟
Convex Functions2分钟
Use of Derivatives3分钟
How Gradient Descent Works3分钟
Gradient Step2分钟
Logistic Regression Algorithm1分钟
Data Visualization and Reading6分钟
Updating Weights in Python4分钟
Implementing Logistic Regression12分钟
Visualization and Results8分钟
Gradient Descent Versus Perceptron4分钟
Linear to Non-Linear Boundaries4分钟
Combining Probabilities2分钟
Weighted Sums3分钟
Neural Network Architecture12分钟
Layers and DEEP Networks4分钟
Multi-Class Classification2分钟
Basics of Feed Forward7分钟
Feed Forward for DEEP Net5分钟
Deep Learning Algo Overview2分钟
Basics of Backpropagation6分钟
Updating Weights2分钟
Chain Rule for Backpropagation5分钟
Sigma Prime2分钟
Data Analysis NN (Neural Networks) Implementation5分钟
One-Hot Encoding (NN Implementation)3分钟
Scaling the Data (NN Implementation)1分钟
Splitting the Data (NN Implementation)5分钟
Helper Functions (NN Implementation)2分钟
Training (NN Implementation)12分钟
Testing (NN Implementation)3分钟

1个作业总计15分钟

Assessment 115分钟

1个插件总计15分钟

Exploring Cross Entropy and Gradient Descent15分钟

In this module, we will address optimization challenges in deep learning. Topics include underfitting vs. overfitting, regularization techniques, and strategies to overcome common issues like local minima and vanishing gradients. Learners will gain insights into improving their model's performance and reliability.

涵盖的内容

10个视频1个插件

10个视频总计38分钟

Underfitting vs Overfitting5分钟
Early Stopping4分钟
Quiz1分钟
Solution and Regularization6分钟
L1 and L2 Regularization3分钟
Dropout3分钟
Local Minima Problem3分钟
Random Restart Solution4分钟
Vanishing Gradient Problem4分钟
Other Activation Functions3分钟

1个插件总计15分钟

Understanding Underfitting and Overfitting in Neural Networks15分钟

In this module, we will undertake a comprehensive final project, applying all the concepts and skills learned throughout the course. Starting with data exploration and progressing through model training and testing, this project will solidify your understanding and ability to implement deep learning solutions.

涵盖的内容

5个视频3个作业

位教师

Packt - Course Instructors

Packt

971 门课程234,954 名学生

提供方

Packt

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常见问题

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

Deep Neural Network for Beginners Using Python

您将学到什么

您将获得的技能

要了解的详细信息

了解顶级公司的员工如何掌握热门技能

该课程共有5个模块

Introduction

涵盖的内容

3个视频总计9分钟

1篇阅读材料总计10分钟

Basics of Deep Learning

涵盖的内容

37个视频总计126分钟

1个插件总计15分钟

Deep Learning

涵盖的内容

31个视频总计153分钟

1个作业总计15分钟

1个插件总计15分钟

Optimizations

涵盖的内容

10个视频总计38分钟

1个插件总计15分钟

Final Project

涵盖的内容

5个视频总计58分钟

3个作业总计90分钟

位教师

提供方

从 Machine Learning 浏览更多内容

Practical Deep Learning with Python

Deep Learning - Crash Course 2023

Introduction to Deep Learning & Neural Networks with Keras

Deep Learning with Keras and Tensorflow

人们为什么选择 Coursera 来帮助自己实现职业发展

通过 Coursera Plus 开启新生涯

通过在线学位推动您的职业生涯

加入超过 3400 家选择 Coursera for Business 的全球公司

常见问题

更多问题

Deep Neural Network for Beginners Using Python

您将学到什么

您将获得的技能

要了解的详细信息

了解顶级公司的员工如何掌握热门技能

该课程共有5个模块

Introduction

涵盖的内容

Basics of Deep Learning

涵盖的内容

Deep Learning

涵盖的内容

Optimizations

涵盖的内容

Final Project

涵盖的内容

位教师

提供方

从 Machine Learning 浏览更多内容

Practical Deep Learning with Python

Deep Learning - Crash Course 2023

Introduction to Deep Learning & Neural Networks with Keras

Deep Learning with Keras and Tensorflow

人们为什么选择 Coursera 来帮助自己实现职业发展

通过 Coursera Plus 开启新生涯

通过在线学位推动您的职业生涯

加入超过 3400 家选择 Coursera for Business 的全球公司

常见问题

Can I preview a course before enrolling?

When will I have access to the lectures and assignments?

What will I get when I enroll?

更多问题