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.
This course is part of the Deep Learning Specialization
Offered By
Deep Learning Specializationdeeplearning.aiAbout this Course
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Learner Career Outcomes
41%
started a new career after completing these courses
37%
got a tangible career benefit from this course
12%
got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 2 of 5 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Beginner Level
Approx. 18 hours to complete
English
Subtitles: Chinese (Traditional), Chinese (Simplified), Portuguese (Brazilian), Korean, Turkish, English, Spanish
Skills you will gain
HyperparameterTensorflowHyperparameter OptimizationDeep Learning
Learner Career Outcomes
41%
started a new career after completing these courses
37%
got a tangible career benefit from this course
12%
got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 2 of 5 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Beginner Level
Approx. 18 hours to complete
English
Subtitles: Chinese (Traditional), Chinese (Simplified), Portuguese (Brazilian), Korean, Turkish, English, Spanish
Offered by
deeplearning.ai
Founded by Andrew Ng, deeplearning.ai is on a mission to make world-class AI education accessible to people around the globe so that we can all benefit from an AI-powered future.
Syllabus - What you will learn from this course
8 hours to complete
Practical aspects of Deep Learning
8 hours to complete
15 videos (Total 131 min), 3 readings, 4 quizzes
15 videos
Bias / Variance8m
Basic Recipe for Machine Learning6m
Regularization9m
Why regularization reduces overfitting?7m
Dropout Regularization9m
Understanding Dropout7m
Other regularization methods8m
Normalizing inputs5m
Vanishing / Exploding gradients6m
Weight Initialization for Deep Networks6m
Numerical approximation of gradients6m
Gradient checking6m
Gradient Checking Implementation Notes5m
Yoshua Bengio interview25m
3 readings
Clarification about Upcoming Regularization Video1m
Clarification about Upcoming Understanding dropout Video1m
Clarification about Upcoming Normalizing Inputs Video1m
1 practice exercise
Practical aspects of deep learning30m
5 hours to complete
Optimization algorithms
5 hours to complete
11 videos (Total 92 min), 2 readings, 2 quizzes
11 videos
Understanding mini-batch gradient descent11m
Exponentially weighted averages5m
Understanding exponentially weighted averages9m
Bias correction in exponentially weighted averages4m
Gradient descent with momentum9m
RMSprop7m
Adam optimization algorithm7m
Learning rate decay6m
The problem of local optima5m
Yuanqing Lin interview13m
2 readings
Clarification about Upcoming Adam Optimization Video1m
Clarification about Learning Rate Decay Video1m
1 practice exercise
Optimization algorithms30m
5 hours to complete
Hyperparameter tuning, Batch Normalization and Programming Frameworks
5 hours to complete
11 videos (Total 104 min), 2 readings, 2 quizzes
11 videos
Using an appropriate scale to pick hyperparameters8m
Hyperparameters tuning in practice: Pandas vs. Caviar6m
Normalizing activations in a network8m
Fitting Batch Norm into a neural network12m
Why does Batch Norm work?11m
Batch Norm at test time5m
Softmax Regression11m
Training a softmax classifier10m
Deep learning frameworks4m
TensorFlow16m
2 readings
Clarifications about Upcoming Softmax Video1m
Note about TensorFlow 1 and TensorFlow 210m
1 practice exercise
Hyperparameter tuning, Batch Normalization, Programming Frameworks30m
Reviews
TOP REVIEWS FROM IMPROVING DEEP NEURAL NETWORKS: HYPERPARAMETER TUNING, REGULARIZATION AND OPTIMIZATION
by XGOct 30, 2017
Thank you Andrew!! I know start to use Tensorflow, however, this tool is not well for a research goal. Maybe, pytorch could be considered in the future!! And let us know how to use pytorch in Windows.
by NAJan 13, 2020
After completion of this course I know which values to look at if my ML model is not performing up to the task. It is a detailed but not too complicated course to understand the parameters used by ML.
by CVDec 23, 2017
Exceptional Course, the Hyper parameters explanations are excellent every tip and advice provided help me so much to build better models, I also really liked the introduction of Tensor Flow Thanks.
by AOct 22, 2017
Assignment in week 2 could not tell the difference between 'a-=b' and 'a=a-b' and marked the former as incorrect even though they are the same and gave the same output. Other than that, a great course
About the Deep Learning 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.
Frequently Asked Questions
When will I have access to the lectures and assignments?
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- The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
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