Who is this course for?

This course is designed for individuals who are familiar with machine learning and want to deepen their knowledge in deep learning and natural language processing. It is ideal for aspiring AI practitioners, data scientists, and software engineers looking to enhance their skills in building advanced AI applications.

How long does it take to complete the course?

The course contains approximately 17 hours of video content. Depending on your pace and the time you dedicate to hands-on practice and projects, you can expect to complete the course in around 3-4 weeks.

Can I preview a course before enrolling?

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.

When will I have access to the lectures and assignments?

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.

What will I get when I enroll?

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.

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 Learning, NLP, and AI Applications

Deep Learning, NLP, and AI Applications

本课程是 AI & Python Development Megaclass 专项课程的一部分

位教师：Packt - Course Instructors

包含在中

了解更多

6个模块

深入了解一个主题并学习基础知识。

高级设置等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

6个模块

深入了解一个主题并学习基础知识。

高级设置等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Build and optimize deep learning models using neural networks, CNNs, and RNNs.
Apply transformers and attention mechanisms to perform advanced NLP tasks.
Utilize transfer learning to fine-tune pre-trained models for specific tasks.
Develop AI-powered applications, including image classifiers, sentiment analyzers, and chatbots.

您将获得的技能

要了解的详细信息

可分享的证书

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

积累特定领域的专业知识

本课程是 AI & Python Development Megaclass 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有6个模块

This course 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. Explore the cutting-edge world of Deep Learning, Natural Language Processing (NLP), and AI applications in this advanced course. You’ll gain hands-on experience with neural networks, CNNs, RNNs, transformers, and other state-of-the-art architectures. Learn to tackle real-world AI tasks such as image classification, sentiment analysis, text summarization, and language translation. This course will guide you through the powerful tools and techniques that are transforming industries, preparing you to build sophisticated AI models. You will start by building foundational knowledge in deep learning, understanding neural networks, forward propagation, and backpropagation. As the course progresses, you’ll work with convolutional neural networks (CNNs) for image recognition, recurrent neural networks (RNNs) for sequence modeling, and transformers for NLP tasks. Additionally, you’ll learn transfer learning to leverage pre-trained models for efficient AI development. This course is designed for learners with a background in machine learning or deep learning who want to expand their expertise into NLP and advanced AI techniques. Whether you’re an AI researcher or aspiring AI engineer, this course will help you apply deep learning to real-world applications. By the end of the course, you will be able to design and implement deep learning models, optimize them for complex AI tasks, and apply cutting-edge NLP techniques to build powerful AI applications.

In this module, we will introduce you to the fundamentals of neural networks and deep learning. You will learn the core components of neural networks, including forward propagation, activation functions, and loss functions. The module also covers backpropagation, gradient descent, and hands-on experience with TensorFlow, Keras, and PyTorch to build and train neural networks. You'll finish by applying your knowledge to an image classification project using CIFAR-10.

涵盖的内容

8个视频2篇阅读材料1个作业

8个视频总计137分钟

Introduction to Week 9 Neural Networks and Deep Learning Fundamentals 1分钟
Day 1: Introduction to Deep Learning and Neural Networks 16分钟
Day 2: Forward Propagation and Activation Functions 15分钟
Day 3: Loss Functions and Backpropagation 16分钟
Day 4: Gradient Descent and Optimization Techniques 22分钟
Day 5: Building Neural Networks with TensorFlow and Keras 19分钟
Day 6: Building Neural Networks with PyTorch 26分钟
Day 7: Neural Network Project – Image Classification on CIFAR-10 22分钟

2篇阅读材料总计20分钟

Introduction to the Course 'Deep Learning, NLP, and AI Applications' 10分钟
Full Specialization Resources 10分钟

1个作业总计15分钟

Neural Networks and Deep Learning Fundamentals - Assessment 15分钟

In this module, we will focus on Convolutional Neural Networks (CNNs), which are essential for computer vision tasks like image recognition. You will learn about the building blocks of CNNs, including convolutional and pooling layers, and explore techniques like regularization and data augmentation to improve model performance. You will apply these concepts to a real-world image classification project using the Fashion MNIST or CIFAR-10 datasets.

涵盖的内容

8个视频1个作业

8个视频总计161分钟

Introduction to Week 10 Convolutional Neural Networks (CNNs) 1分钟
Day 1: Introduction to Convolutional Neural Networks 26分钟
Day 2: Convolutional Layers and Filters 24分钟
Day 3: Pooling Layers and Dimensionality Reduction 24分钟
Day 4: Building CNN Architectures with Keras and TensorFlow 18分钟
Day 5: Building CNN Architectures with PyTorch 22分钟
Day 6: Regularization and Data Augmentation for CNNs 19分钟
Day 7: CNN Project – Image Classification on Fashion MNIST or CIFAR-10 28分钟

1个作业总计15分钟

Convolutional Neural Networks (CNNs) - Assessment 15分钟

In this module, we will delve into Recurrent Neural Networks (RNNs) and sequence modeling. You will learn about RNN architectures, including LSTMs and GRUs, and their ability to handle sequential data. The module covers how to train these networks with backpropagation through time and applies them to text-based tasks like sentiment analysis and text generation.

涵盖的内容

8个视频1个作业

8个视频总计166分钟

Introduction to Week 11: Recurrent Neural Networks (RNNs) and Sequence Modeling 1分钟
Day 1: Introduction to Sequence Modeling and RNNs 34分钟
Day 2: Understanding RNN Architecture and Backpropagation Through Time (BPTT) 25分钟
Day 3: Long Short-Term Memory (LSTM) Networks 15分钟
Day 4: Gated Recurrent Units (GRUs) 7分钟
Day 5: Text Preprocessing and Word Embeddings for RNNs 24分钟
Day 6: Sequence-to-Sequence Models and Applications 43分钟
Day 7: RNN Project – Text Generation or Sentiment Analysis 18分钟

1个作业总计15分钟

Recurrent Neural Networks (RNNs) and Sequence Modeling - Assessment 15分钟

In this module, we will explore transformers and attention mechanisms, which have revolutionized the field of Natural Language Processing (NLP). You will gain a deep understanding of the transformer architecture, including self-attention and multi-head attention. The module also covers practical applications with pre-trained models like BERT and GPT for NLP tasks such as text summarization and machine translation.

涵盖的内容

8个视频1个作业

8个视频总计135分钟

Introduction to Week 12: Transformers and Attention Mechanisms 1分钟
Day 1: Introduction to Attention Mechanisms 15分钟
Day 2: Introduction to Transformers Architecture 18分钟
Day 3: Self-Attention and Multi-Head Attention in Transformers 21分钟
Day 4: Positional Encoding and Feed-Forward Networks 20分钟
Day 5: Hands-On with Pre-Trained Transformers – BERT and GPT 20分钟
Day 6: Advanced Transformers – BERT Variants and GPT-3 21分钟
Day 7: Transformer Project – Text Summarization or Translation 19分钟

1个作业总计15分钟

Transformers and Attention Mechanisms - Assessment 15分钟

In this module, we will focus on transfer learning and fine-tuning techniques that allow you to leverage pre-trained models for new tasks. You will learn how to fine-tune models in both computer vision and NLP and tackle challenges like domain adaptation. The module includes hands-on projects where you will apply transfer learning techniques to custom tasks, enhancing the performance of your models.

涵盖的内容

8个视频1个作业

8个视频总计140分钟

Introduction to Week 13: Transfer Learning and Fine-Tuning 1分钟
Day 1: Introduction to Transfer Learning 15分钟
Day 2: Transfer Learning in Computer Vision 26分钟
Day 3: Fine-Tuning Techniques in Computer Vision 22分钟
Day 4: Transfer Learning in NLP 17分钟
Day 5: Fine-Tuning Techniques in NLP 26分钟
Day 6: Domain Adaptation and Transfer Learning Challenges 15分钟
Day 7: Transfer Learning Project – Fine-Tuning for a Custom Task 18分钟

1个作业总计15分钟

Transfer Learning and Fine-Tuning - Assessment 15分钟

In this section, we will guide you through a series of exciting AI and machine learning projects. You will build applications like a spam email detector, sentiment analyzer, voice assistant, and object detection app. These projects provide hands-on experience in solving real-world problems with deep learning and machine learning techniques, solidifying your understanding of the concepts learned throughout the course.

涵盖的内容

10个视频1篇阅读材料3个作业

10个视频总计157分钟

Day 71: Spam Email Detector 23分钟
Day 72: Text Sentiment Analyzer 12分钟
Day 73: Handwriting Digit Recognition 25分钟
Day 74: Voice Assistant 17分钟
Day 75: Face Detection App 19分钟
Day 76: Simple Recommendation System 14分钟
Day 77: AI Chatbot with NLP 13分钟
Day 78: Object Detection App 11分钟
Day 79: Language Translator Tool 11分钟
Day 80: Fake News Detector 14分钟

1篇阅读材料总计10分钟

Conclusion to the Course 'Deep Learning, NLP, and AI Applications' 10分钟

3个作业总计90分钟

Full Course Practice Assessment 15分钟
AI & Machine Learning Projects - Assessment 15分钟
Full Course Assessment 60分钟

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常见问题

This course focuses on the key concepts and techniques in deep learning and natural language processing (NLP), two core areas driving the development of modern AI applications. Understanding deep learning and NLP is crucial for developing systems that can perform complex tasks like image recognition, speech processing, and language translation. These skills are highly sought after in industries like healthcare, finance, and technology.

This course covers the fundamentals of deep learning, including neural networks, CNNs, RNNs, and transformers, as well as their applications in NLP. You will learn to build and optimize models for various AI tasks such as image classification, text generation, sentiment analysis, and language translation. The course also explores advanced topics like transfer learning, fine-tuning, and working with pre-trained models, equipping you with the tools to tackle real-world AI challenges.

After completing the course, you will be able to design and implement deep learning models for a variety of AI applications, including computer vision tasks (e.g., image classification), NLP tasks (e.g., text summarization, sentiment analysis), and even transfer learning for specific domains. You will also be skilled in using popular deep learning frameworks like TensorFlow, Keras, and PyTorch to build, train, and optimize models.

To get the most out of this course, you should have a basic understanding of machine learning and programming, particularly in Python. Familiarity with concepts like supervised learning and neural networks will be helpful. Some knowledge of linear algebra, calculus, and probability/statistics is beneficial, as these are foundational to understanding the underlying principles of deep learning models.

Deep Learning, NLP, and AI Applications

Deep Learning, NLP, and AI Applications

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

积累特定领域的专业知识

该课程共有6个模块

Neural Networks and Deep Learning Fundamentals

涵盖的内容

Convolutional Neural Networks (CNNs)

涵盖的内容

Recurrent Neural Networks (RNNs) and Sequence Modeling

涵盖的内容

Transformers and Attention Mechanisms

涵盖的内容

Transfer Learning and Fine-Tuning

涵盖的内容

AI & Machine Learning Projects

涵盖的内容

获得职业证书

位教师

提供方

从 Software Development 浏览更多内容

Deep Learning & Modern AI Architectures

Natural Language Processing - Deep Learning Models in Python

Natural Language Processing

AI & Deep Learning Concepts and Applications

人们为什么选择 Coursera 来帮助自己实现职业发展

Felipe M.

Jennifer J.

Larry W.

Chaitanya A.

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

What is Deep Learning, NLP, and AI Applications? Why is it relevant?

What is this course about?

What will I be able to do after completing this course?

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