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LifestyleImproving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
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
Created by: Andrew Ng
Course Description
Instructor Details
- 5.0 Rating
526 Reviews
Andrew Ng
Andrew Ng is Co-founder of Coursera, an and Adjunct Professor of Computer Science at Stanford University. His machine learning course is the MOOC that had led to the founding of Coursera! In 2011, he led the development of Stanford University's main MOOC (Massive Open Online Courses) platform and also taught an online Machine Learning class to over 100,000 students, thus helping launch the MOOC movement and also leading to the founding of Coursera.Ng also works on machine learning, with an emphasis on deep learning. He had founded and led the "Google Brain" project, which developed massive-scale deep learning algorithms. This resulted in the famous "Google cat" result, in which a massive neural network with 1 billion parameters learned from unlabeled YouTube videos to detect cats. Until recently, he led Baidu's ~1300 person AI Group, which developed technologies in deep learning, speech, computer vision, NLP, and other areas.
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Machine Learning Andrew Ng, Stanford University [FULL COURSE] (2016)
Provider: YouTube
Time: 19h
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Reviews
By Sriram V on 9-Oct-19
Insights into best practices and directions for common problems make it an one-of-a-kind material for learners. Andrew, as always, has been commendable with his tutor team, the exercises are well cleaned up and in good shape. May be, if some optional tough exercises are given, it will add more value.
By Artyom K on 9-May-19
The topics of this course, such as the setting of hyperparameters and the use of tensorflow, are critical topics for me, and in this course they are explained both in lectures and in practical tasks.
By Matthew G on 18-Apr-19
Very good course. Andrew really steps it up in part two with lots of valuable information.
By Md. R K S on 15-Apr-19
Excellent course. When I learned about implementing ANN using keras in python, I just followed some tutorials but didn't understand the tradeoff among many parameters like the number of layers, nodes per layers, epochs, batch size, etc. This course is helping me a lot to understand them. Great work Mr. Andrew Ng. :)
By Mohamadreza on 14-Apr-19
That was AWSOME! It was really good and I'm pretty sure it would help me through my study and career!Thanks!
By Arun S on 13-Apr-19
This course helped me to understand the practical aspect of NN. Tuning of Hyper parameters, Regularization , Algos like ADAM are important for fast and accurate training. I hope i could make use of information in future. However this course gives very little introduction to tensorflow and somewhat doesn't satisfies students i believe. Prof. Andrew Ng gives a fantastic lectures covering all important aspects in details with patience.
By Yan on 13-Apr-19
Although the concepts of deep learning ( ie. the gradient descent, the chain rule ) are quite easy-understanding and clear to most people, how to choose the hyperparameter and how to effectively carry out the projects are real essence. That's what I learn from this course. Thanks for so many genius researchers contributing to this area.
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Quality Score
Overall Score : 100 / 100
- Video Duration:
15h - Price: Free
- Provider: Coursera
- University:
deeplearning.ai - Language:
English - Certificate:
Yes
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