Deep Learning is a subset of Machine learning that utilizes multi-layer Artificial Neural Networks. Deep Learning is inspired by the human brain and mimics the operation of biological neurons. A hierarchical, deep artificial neural network is formed by connecting multiple artificial neurons in a layered fashion..If you are interested in deep learning and you want to learn about modern deep learning developments beyond just plain back propagation, including using unsupervised neural networks to interpret what features can be automatically and hierarchically learned in a deep learning system,
Deep learning vs. machine learning
If deep learning is a subset of machine learning, how do they differ? Deep learning distinguishes itself from classical machine learning by the type of data that it works with and the methods in which it learns.
Machine learning algorithms leverage structured, labeled data to make predictions—meaning that specific features are defined from the input data for the model and organized into tables. This doesn’t necessarily mean that it doesn’t use unstructured data; it just means that if it does, it generally goes through some pre-processing to organize it into a structured format.
Deep learning eliminates some of data pre-processing that is typically involved with machine learning. These algorithms can ingest and process unstructured data, like text and images, and it automates feature extraction, removing some of the dependency on human experts. For example, let’s say that we had a set of photos of different pets, and we wanted to categorize by “cat”, “dog”, “hamster”, et cetera. Deep learning algorithms can determine which features (e.g. ears) are most important to distinguish each animal from another. In machine learning, this hierarchy of features is established manually by a human expert.
In this course is for you.after this course you’ll be able to fill your resume with skills and have plenty left over to show off at the interview.
1.Understand the back propagation process, intuitively and mathematically.
2.Learn how to build deep neural networks using real data, implemented by real companies in the real world.
3. It’s this hands-on experience that will really make your resume stand out.