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.
This course is part of the Deep Learning Specialization
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Offered By
About this Course
4.9
25,389 ratings
Skills you will gain
HyperparameterTensorflowHyperparameter OptimizationDeep Learning
Syllabus - What you will learn from this course
Week
1Practical aspects of Deep Learning
15 videos (Total 131 min), 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
1 practice exercise
Practical aspects of deep learning20m
Week
2Optimization algorithms
11 videos (Total 92 min), 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
1 practice exercise
Optimization algorithms20m
Week
3Hyperparameter tuning, Batch Normalization and Programming Frameworks
11 videos (Total 104 min), 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
1 practice exercise
Hyperparameter tuning, Batch Normalization, Programming Frameworks20m
36%
83%
Top Reviews
By CV•Dec 24th 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\n\nThanks.
By PG•Oct 31st 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.
Instructors
Andrew Ng
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
Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
Teaching Assistant - Younes Bensouda Mourri
Mathematical & Computational Sciences, Stanford University, deeplearning.aiAbout deeplearning.ai
deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
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?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
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?
Is financial aid available?
More questions? Visit the Learner Help Center.
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