When I Searched and google the internet I found out this Cousera course and fell in love with the name. It's now a long time I enrolled in a Mooc course online. In another post I will share about my love for Mooc courses, even though some are free, they are well designed to educate you just like a university would or even much more.
I will consider an academic organization like Cousera to be offering Mooc courses. So about this course called 'Deep Learning', the below information would guide you on what to expect in the course. As I dig into the Cousera website, I discover that you could be required to pay for the course and the option to just read or audit the course is available. However, to learn better you may want to pay so that you can have access to workshops, other tasks, assignments, discussion forums, printable certificate e.t.c that would broaden your eventual learning outcome. The beauty and relevance of this course is that it is precursor to delving into the field and knowledge about 'Artificial Intelligence' popular known by its acronym IA. Actually, I think many people want to learn or simply have the interest for IA but don't know the simplest method to go about it. I will highly suggest, you start with this course 'Deep Learning'.
Read futher about 'Deep Learning':
Offered By: Coursera
Course Name: Deep Learning
Rating is very high, Certificate is available, Price is applicable, you can also learn for free.
SKILLS YOU WILL GAIN ARE:
Tensorflow
Convolutional Neural Network
Artificial Neural Network
Deep Learning
Backpropagation
Python Programming
Hyperparameter
Hyperparameter Optimization
Machine Learning
Inductive Transfer
Multi-Task Learning
Facial Recognition System
Statements of Assurance:
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.
There are 5 Courses in this Specialization as follows:
COURSE 1
Neural Networks and Deep Learning:
If 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" that will let you build AI systems that just weren't possible a few years ago.
In this course, you will learn the foundations of deep learning. When you finish this class, you will: - Understand the major technology trends driving Deep Learning - Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions. This is the first course of the Deep Learning Specialization.
COURSE 2
Improving Deep Neutral Networks;
Hyperparameter tuning, Regularization and Optimization:
This 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 also learn TensorFlow.
After 3 weeks, you will: - Understand industry best-practices for building deep learning applications. - Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking, - Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence. - Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance - Be able to implement a neural network in TensorFlow. This is the second course of the Deep Learning Specialization.
COURSE 3
Structuring Machine Learning Projects:
You 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 is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience. After 2 weeks, you will: - Understand how to diagnose errors in a machine learning system, and - Be able to prioritize the most promising directions for reducing error - Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance - Know how to apply end-to-end learning, transfer learning, and multi-task learning I've seen teams waste months or years through not understanding the principles taught in this course. I hope this two week course will save you months of time. This is a standalone course, and you can take this so long as you have basic machine learning knowledge. This is the third course in the Deep Learning Specialization.
COURSE 4
Convolutional Neural Networks:
This 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.
Applied Learning Project
You will see and work on case studies in healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will also build near state-of-the-art deep learning models for several of these applications. In a "Machine Learning flight simulator", you will work through case studies and gain "industry-like experience" setting direction for an ML team.
deeplearning.ai is also partnering with the NVIDIA Deep Learning Institute (DLI) in Course 5, Sequence Models, to provide a programming assignment on Machine Translation with deep learning. You will have the opportunity to build a deep learning project with cutting-edge, industry-relevant content.
TESTIMONIAL AND LEARNER CAREER OUTCOMES
41% got a new career after completing this specialization.
14% got a pay increase or promotion.
Shareable Certificate
100% online courses
Flexible Schedule
Set and maintain flexible deadlines.
Some related experience required.
Approx. 4 months to complete
Suggested 5 hours/week
Subtitles in: English, Chinese
(Traditional), Arabic, French,
Ukrainian, Chinese
(Simplified), Portuguese
(Brazilian), Vietnamese,
Korean, Turkish, Spanish,
Japanese
Instructors
Andrew Ng
TOP INSTRUCTOR
CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist, Baidu and founding lead of Google Brain
Head Teaching Assistant - Kian Katanforoosh
TOP INSTRUCTOR
Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
Teaching Assistant - Younes Bensouda Mourri
TOP INSTRUCTOR
Mathematical & Computational Sciences, Stanford University, deeplearning.ai
Computer Science
Read futher about 'Deep Learning':
Offered By: Coursera
Course Name: Deep Learning
Rating is very high, Certificate is available, Price is applicable, you can also learn for free.
SKILLS YOU WILL GAIN ARE:
Tensorflow
Convolutional Neural Network
Artificial Neural Network
Deep Learning
Backpropagation
Python Programming
Hyperparameter
Hyperparameter Optimization
Machine Learning
Inductive Transfer
Multi-Task Learning
Facial Recognition System
Statements of Assurance:
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.
There are 5 Courses in this Specialization as follows:
COURSE 1
Neural Networks and Deep Learning:
If 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" that will let you build AI systems that just weren't possible a few years ago.
In this course, you will learn the foundations of deep learning. When you finish this class, you will: - Understand the major technology trends driving Deep Learning - Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions. This is the first course of the Deep Learning Specialization.
COURSE 2
Improving Deep Neutral Networks;
Hyperparameter tuning, Regularization and Optimization:
This 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 also learn TensorFlow.
After 3 weeks, you will: - Understand industry best-practices for building deep learning applications. - Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking, - Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence. - Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance - Be able to implement a neural network in TensorFlow. This is the second course of the Deep Learning Specialization.
COURSE 3
Structuring Machine Learning Projects:
You 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 is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience. After 2 weeks, you will: - Understand how to diagnose errors in a machine learning system, and - Be able to prioritize the most promising directions for reducing error - Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance - Know how to apply end-to-end learning, transfer learning, and multi-task learning I've seen teams waste months or years through not understanding the principles taught in this course. I hope this two week course will save you months of time. This is a standalone course, and you can take this so long as you have basic machine learning knowledge. This is the third course in the Deep Learning Specialization.
COURSE 4
Convolutional Neural Networks:
This 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.
Applied Learning Project
You will see and work on case studies in healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will also build near state-of-the-art deep learning models for several of these applications. In a "Machine Learning flight simulator", you will work through case studies and gain "industry-like experience" setting direction for an ML team.
deeplearning.ai is also partnering with the NVIDIA Deep Learning Institute (DLI) in Course 5, Sequence Models, to provide a programming assignment on Machine Translation with deep learning. You will have the opportunity to build a deep learning project with cutting-edge, industry-relevant content.
TESTIMONIAL AND LEARNER CAREER OUTCOMES
41% got a new career after completing this specialization.
14% got a pay increase or promotion.
Shareable Certificate
100% online courses
Flexible Schedule
Set and maintain flexible deadlines.
Some related experience required.
Approx. 4 months to complete
Suggested 5 hours/week
Subtitles in: English, Chinese
(Traditional), Arabic, French,
Ukrainian, Chinese
(Simplified), Portuguese
(Brazilian), Vietnamese,
Korean, Turkish, Spanish,
Japanese
Instructors
Andrew Ng
TOP INSTRUCTOR
CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist, Baidu and founding lead of Google Brain
Head Teaching Assistant - Kian Katanforoosh
TOP INSTRUCTOR
Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
Teaching Assistant - Younes Bensouda Mourri
TOP INSTRUCTOR
Mathematical & Computational Sciences, Stanford University, deeplearning.ai
Computer Science
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