Deep Learning
Deep learning has already transformed traditional internet businesses like web search and advertising. But it is also enabling brand new products, businesses, and ways of helping people. Applications range from better healthcare (where deep learning is getting very good at reading X-ray images), to delivering personalized education, to precision agriculture, to self-driving cars, and many others.
These notes cover the Deep Learning Specialization by Andrew Ng (DeepLearning.AI).
AI is the New Electricity
Starting about 100 years ago, the electrification of society transformed every major industry: transportation, manufacturing, healthcare, communications, and more. Today, we see a surprisingly clear path for AI to bring about an equally big transformation.
The part of AI that is rising rapidly and driving a lot of these developments is deep learning. It is one of the most highly sought-after skills in the technology world today.
Specialization Roadmap
The Deep Learning Specialization consists of five courses. Here is what each one covers:
Course 1: Neural Networks and Deep Learning
This is the foundational course. You will learn:
- The foundations of neural networks
- How to build a neural network, including a deep neural network
- How to train it on data
By the end, you will be able to build a deep neural network to recognize cats (following a long-standing tradition in the deep learning community).
Course 2: Improving Deep Neural Networks
Once you have built a neural network, how do you actually get it to perform well? This course covers the practical aspects:
- Hyperparameter tuning
- Regularization
- Diagnosing bias and variance
- Advanced optimization algorithms (momentum, RMSProp, Adam)
A lot of building neural networks can feel like black magic. This course demystifies that process.
Course 3: Structuring Machine Learning Projects
The strategy for building a machine learning system has changed in the era of deep learning. You will learn:
- How to split data into train, development (dev/holdout/cross-validation), and test sets using modern best practices
- How to handle training and test sets that come from different distributions
- When to use (and when not to use) end-to-end deep learning
This course draws from hard-won lessons in building and shipping deep learning products in production.
Course 4: Convolutional Neural Networks (CNNs)
Convolutional neural networks are often applied to images. You will learn how to build these models and apply them to computer vision tasks.
Course 5: Sequence Models
Sequence models handle sequential data like text and audio. You will learn about:
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory (LSTM) models
- Applications to natural language processing, speech recognition, and music generation
Natural language is just a sequence of words, and these models are specifically designed to work with that kind of sequential structure.