Neural Networks and Deep Learning
Learn how to build and implement your own deep neural networks in just 7 hours. Taught by an experienced instructor, this is the first course in the Deep Learning Specialization.
Created by: Andrew Ng
Produced in 2017
What you will learn
- Build a deep neural network that can recognize cats
- Implement vectorization to neural network models
- Learn how to use backpropagation and forward propagation
- Create one-hidden-layer neural networks
- Understand the difference between parameters and hyperparameters
- Understand how deep learning works
- Much, Much more!
Overall Score : 94 / 100
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machine learning Awards Best Advanced Course
- Offered by deeplearning.ai, a well known provider of a world-class AI education.
- Taught in Python and Jupyter Notebook.
- Good introduction to how to build and implement neural networks.
- Easy to understand lectures with a mix of theory and practical application.
- Useful tips and insights into Deep Learning.
- Pre-written code in assignments.
- Repetitive content.
- 4.7 Rating
- 132 Reviews
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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