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<UL><LI></LI></UL><h2><span>About this Specialization</span></h2><div><span dir="ltr"><span dir="ltr">381,289</span> recent views</span></div><div>The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.

In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.

AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia.</div><h2>Applied Learning Project</h2><p>By the end you’ll be able to</p><p> • Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply DL to your applications</p><p>• 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</p><p>• Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning</p><p>• 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</p><p>• 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</p><div>Learner Career Outcomes</div><div>Shareable Certificate</div><div>Earn a Certificate upon completion</div><div>100% online courses</div><div>Start instantly and learn at your own schedule.</div><div>Flexible Schedule</div><div>Set and maintain flexible deadlines.</div><div>Intermediate Level</div><UL><LI>Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures</LI><LI>A basic grasp of linear algebra &amp; ML</LI></UL><div><span>Approximately 5 months to complete</span></div><div><span>Suggested pace of 8 hours/week</span></div><div>English</div><div><span>Subtitles: English, Chinese (Traditional), Arabic, French, Ukrainian, Portuguese (European), Chinese (Simplified), Italian, Portuguese (Brazilian), Vietnamese, Korean, German, Russian, Turkish, Spanish, Japanese</span></div><UL><LI><button aria-label="Carousel slide 1" aria-current="false" data-track="true" data-track-app="xdp_v1" data-track-page="xdp" data-track-action="[object Object]" data-track-component="testimonial_slider_dot"><span>1</span></button></LI><LI><button aria-label="Carousel slide 2" aria-current="true" data-track="true" data-track-app="xdp_v1" data-track-page="xdp" data-track-action="[object Object]" data-track-component="testimonial_slider_dot"><span>2</span></button></LI><LI><button aria-label="Carousel slide 3" aria-current="false" data-track="true" data-track-app="xdp_v1" data-track-page="xdp" data-track-action="[object Object]" data-track-component="testimonial_slider_dot"><span>3</span></button></LI></UL><h2>How the Specialization Works</h2><h3>Take Courses</h3><p>A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.</p><h3>Hands-on Project</h3><p>Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.</p><h3>Earn a Certificate</h3><p>When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.</p><img src="https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera_assets.s3.amazonaws.com/lady_looking_at_computer.png?auto=format%2Ccompress&amp;dpr=1&amp;h=350&amp;w=350&amp;px=16&amp;blur=200" alt="" width="552" height="536"><h2><span>There are 5 Courses in this Specialization</span></h2><div><strong>Course</strong></div><div><span>1</span></div>

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Offered by DeepLearning.AI. Become a Machine Learning expert. Master the fundamentals of deep learning and break into AI. Recently updated ... Enroll for free.

personaldevelopment

Personal Development

technologyandthefuture

Technology & The Future

problemsolving

Problem Solving

computerscience

Computer Science

artificialintelligence

Artificial Intelligence

<p>• Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply DL to your applications </p>

<p>• 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 </p>

<p>• Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning </p>

<p>• 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 </p>

<p>• 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 </p>

<ul><li>Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures </li><li>A basic grasp of linear algebra & ML </li> </ul>

<p>A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress. </p>

<p>Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it. </p>

<p>When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network. </p>

There are 5 Courses in this Specialization

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