Deep Learning Specialization
Master Deep Learning, and Break into AI
About This Specialization
If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning. In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach. You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice. AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work. We will help you master Deep Learning, understand how to apply it, and build a career in AI.
Follow the suggested order or choose your own.
Designed to help you practice and apply the skills you learn.
Highlight your new skills on your resume or LinkedIn.
- Intermediate Specialization.
- Some related experience required.
Neural Networks and Deep LearningUpcoming session: Nov 7
- 4 weeks of study, 3-6 hours a week
- English, Chinese (Traditional)
About the CourseIf you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" t
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and OptimizationUpcoming session: Nov 7
- 3 weeks, 3-6 hours per week
About the CourseThis course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will al
Structuring Machine Learning ProjectsUpcoming session: Nov 7
- 2 weeks of study, 3-4 hours/week
About the CourseYou will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been taught elsewhere, and
Convolutional Neural NetworksStarts late October 2017
About the CourseThis course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. You will: - Understand how to build a convolutional neural network, including recent variations such as residual networks. - Know how to apply convolutional networks to visual detection and recognition tasks. - Know to use neural style transfer to generate art. - Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data. This is the fourth course of the Deep Learning Specialization.
Sequence ModelsStarts November 2017
About the CourseThis course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others. You will: - Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. - Be able to apply sequence models to natural language problems, including text synthesis. - Be able to apply sequence models to audio applications, including speech recognition and music synthesis. This is the fifth and final course of the Deep Learning Specialization.
Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
Teaching Assistant - Younes Bensouda Mourri
Mathematical & Computational Sciences, Stanford University
Teaching Assistant - Kian Katanforoosh
M.S. Stanford University (Walter J. Gores 2017), B.S Ecole Centrale Paris
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