Deep Learning Specialization provides an introduction to DL methods for computer vision applications for practitioners who are familiar with the basics of DL.
You will discover a breakdown and review of the convolutional neural networks course taught by Andrew Ng on deep learning specialization. It does not focus too much on math and does not include any code.
After finishing the specialization you will know how to build models for photo classification, object detection, face recognition, and more.
Instructors patiently explain the requisite math and programming concepts in a carefully planned order for learners who could be rusty in math/coding.
Coursera Deep Learning Specialization Review
Table of Contents
Why You Should Take Coursera Deeplearning ai
The Coursera Deep Learning is designed to educate Deep Learning in a simple way in order to boost up the development of Artificial Intelligence.
These five courses are a step by step series to cover all fundamental aspects of deep learning although you could only take those you are interested in.
It is absolutely suitable for Deep Learning beginner with fundamental Python Programming skills. It will cover the topics
- Convolutional Neural Network
- Artificial Neural Network
- Deep Learning
Deep Learning Specialization on Coursera Instructors
This online Specialization is taught by three instructors.
- Andrew Ng
CEO/Founder Landing AI, Co-founder of Coursera, Professor of Stanford University, formerly Chief Scientist of Baidu and founding lead of Google Brain
- Kian Katanforoosh
Lecturer of Computer Science at Stanford University, deeplearning.ai
- Younes Bensouda Mourri
Mathematical & Computational Sciences, Stanford University, deeplearning.ai
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 the following,
- Convolutional Neural Network
- Artificial Neural Network
- Deep Learning
You can work on case studies from
- Autonomous driving
- Sign language reading
- Music generation and
- Natural language processing
After finishing the specialization you will expert not only the theory but also see how it is applied in industry. You can practice all the ideas in Python and in TensorFlow.
About 248k+ students have already enrolled in this online specialization.
Process of Taking Coursera Deep Learning
The course is taught in Python. If you have basic programming skills (understanding of for loops, if/else statements, data structures such as lists and dictionaries), this course is for you.
– Mathematics: Basic linear algebra will help you to understand the specialization.
– Machine Learning: A basic knowledge of machine learning (how to present data, what does a machine learning model work) will help.
Quizzes are taken at the end of each lecture section and are in the multiple-choice question type format. If you watch the videos once, you will be able to quickly answer all the quiz questions.
You can attempt quizzes multiple times and the system is designed to keep your highest score.
Courses in this Specialization
There are 5 Courses in this Specialization.
1. Deep Learning and Neural Network
In course 1, you know about what is Neural Network, Forward & Backward Propagation and guide you to build a shallow network, then stack it to be a deep network.
Also, you will learn about mathematics (Logistics Regression, Gradient Descent and etc.) related to it in several steps.
The instructor explains the maths in a very simple way that you would understand it without prior knowledge in linear algebra and calculus.
When you finish this Specialization, you will understand the major technology trends driving Deep Learning -Be able to build, train and apply fully connected deep neural networks.
2. Improving Deep Neural Networks: Hyper-parameter Tuning, Regularization and Optimization
In tutorial 2, you will learn different regularization techniques. In the first portion of the course, you will know how to evaluate your deep learning model and note down the hyper-parameter tuning technique in different situations.
Favorite topics of this tutorial are the Mini batch application and how it affects the model.
After completing Coursera deep learning you will concern about the industry best-practices for building deep learning applications, be able to effectively use the common neural network “tricks”.
3. Structuring Machine Learning Projects
In the number 3 tutorial, you will learn how to set up an evaluation metric. In this tutorial, it has a discussion on how to select and split the train/test/validation set and the methods you could use while lacking data.
The focused topics in this tutorial are Error analysis table which drills down the prediction results to look for model tuning insights.
4. Convolutional Neural Network
In online tutorial 4, you will learn about what is computer vision and how does a Convolutional Neural Network work by explaining the theory and math of convolutional filter, Maxpooling filter, etc.
Focused topics in this course are Image localization and detection.
5. Sequence Models
In the last course, you will learn how to build models for natural language, audio, and other sequence data.
Deep Learning, sequence algorithms are working far better than the years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others.
Lastly, you could practice how to build a machine translation model. In the last course, the favorite topics are Trigger Detection with the self-made audio data.
Earn a Career Credential
After you accomplished the courses it would issue 5 course certifications plus one deep learning specialization certification which could directly attach to your LinkedIn profile.
Every Specialization includes a hands-on project. You will need to successfully finish the project(s) to complete the Specialization and earn a certificate.
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.
These Career Credentials will help you to unlock access to work in top universities and organizations as well as you can get a chance to get a career credential from the world’s best educational institution.
Deep Learning Career Opportunities
After taking this course, Deep Learning talent would pop out and also Deep Learning knowledge enables you to complete the topic you are interested in and connecting you to the entry of this industry.
This specialization would recommend to everyone who is interested in Deep Learning and not only for the beginner but for those who have knowledge in this field as well.
Knowledge consolidation is always good and teaches you new stuff. There are many career paths in Deep Learning ai that are popular and well-paying such as;
- Software Analysts and Developers.
- Computer Scientists and Computer Engineers.
- Algorithm Specialists.
- Research Scientists and Engineering Consultants.
- Mechanical Engineers and Maintenance Technicians.
- Manufacturing and Electrical Engineers.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, you will not get refunds, but you can cancel your subscription at any time.
Each course would have a four-week syllabus on an average which requires to devote you 2 to 4 hour a week. The workload is not big at all for people who have a full-time job.
And the course fee is only $49 per month with 7 days free trial which is one of the cheapest MOOC courses.
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Farzana Ahmed Sabera, Working as Digital Marketing Executive at Reinforce Lab. Writer at JA DIRECTIVES with diverse knowledge of writing content and articles. Completed Bachelor of Science in Computer Science & Engineering, from University of Asia Pacific, Dhaka, Bangladesh. Done several research works focused on Digital Image Processing. Published research paper on International Journal. Always love to work with new technologies.