- Artificial Neural Network
- Convolutional Neural Network
- Tensorflow
- Recurrent Neural Network
- Transformers
- Deep Learning
- Backpropagation
- Python Programming
- Neural Network Architecture
- Mathematical Optimization
- hyperparameter tuning
- Inductive Transfer
심층 학습 특화 과정
Become a Machine Learning expert. Master the fundamentals of deep learning and break into AI. Recently updated with cutting-edge techniques!
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배울 내용
Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications
Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow
Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data
Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering
귀하가 습득할 기술
이 전문 분야 정보
응용 학습 프로젝트
By the end you’ll be able to
• Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply DL to your applications
• Use best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard NN techniques, apply optimization algorithms, and implement a neural network in TensorFlow
• Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning
• Build a Convolutional Neural Network, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data
• Build and train Recurrent Neural Networks and its variants (GRUs, LSTMs), apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform Named Entity Recognition and Question Answering
- Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
- A basic grasp of linear algebra & ML
- Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
- A basic grasp of linear algebra & ML
특화 과정 이용 방법
강좌 수강
Coursera 특화 과정은 한 가지 기술을 완벽하게 습득하는 데 도움이 되는 일련의 강좌입니다. 시작하려면 특화 과정에 직접 등록하거나 강좌를 둘러보고 원하는 강좌를 선택하세요. 특화 과정에 속하는 강좌에 등록하면 해당 특화 과정 전체에 자동으로 등록됩니다. 단 하나의 강좌만 수료할 수도 있으며, 학습을 일시 중지하거나 언제든 구독을 종료할 수 있습니다. 학습자 대시보드를 방문하여 강좌 등록 상태와 진도를 추적해 보세요.
실습 프로젝트
모든 특화 과정에는 실습 프로젝트가 포함되어 있습니다. 특화 과정을 완료하고 수료증을 받으려면 프로젝트를 성공적으로 마쳐야 합니다. 특화 과정에 별도의 실습 프로젝트 강좌가 포함되어 있는 경우, 다른 모든 강좌를 완료해야 프로젝트 강좌를 시작할 수 있습니다.
수료증 취득
모든 강좌를 마치고 실습 프로젝트를 완료하면 취업할 때나 전문가 네트워크에 진입할 때 제시할 수 있는 수료증을 취득할 수 있습니다.

이 전문 분야에는 5개의 강좌가 있습니다.
신경망 및 딥 러닝
In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically.
Structuring Machine Learning Projects
In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.
Convolutional Neural Networks
In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more.
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deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
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해당 강좌를 무료로 수강할 수 있나요?
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전문 분야를 완료하면 대학 학점을 받을 수 있나요?
What is Deep Learning? Why is it relevant?
What is the Deep Learning Specialization about?
What will I be able to do after completing the Deep Learning Specialization?
What background knowledge is necessary for the Deep Learning Specialization?
Who is the Deep Learning Specialization for?
How long does it take to complete the Deep Learning Specialization?
Who is the Deep Learning Specialization by?
Is this a standalone course or a Specialization?
Do I need to take the courses in a specific order?
Can I apply for financial aid?
Can I audit the Deep Learning Specialization?
How do I get a receipt to get this reimbursed by my employer?
I want to purchase this Specialization for my employees! How can I do that?
The Deep Learning Specialization was updated in April 2021. What is different in the new version?
I’m currently enrolled in one or more courses in the Deep Learning Specialization. What does this mean for me?
I’ve already completed one or more courses in the Deep Learning Specialization but don’t have an active subscription. What does this mean for me?
Can I get college credit for taking the Deep Learning Specialization?
How do I pursue the ACE credit recommendation?
How do I know which colleges and universities grant credit for the Deep Learning Specialization?
궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.