If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new “superpower” that will let you build AI systems that just weren’t possible a few years ago. In this course, you will learn the foundations of deep learning.
When you finish this class, you will:
– Understand the major technology trends driving Deep Learning
– Be able to build, train and apply fully connected deep neural networks
– Know how to implement efficient (vectorized) neural networks
– Understand the key parameters in a neural network’s architecture
This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions.
Who is this class for: Prerequisites: – Basic programming: You’ll practice programming neural networks in Python. So long as you know the basics of programming (for/while loops, function calls, etc.), you should be able to pick up the needed Python. – Basic machine Learning knowledge: Any prior knowledge would help, though it’s not hard for to pick it all up within this course. If you have taken my Machine Learning Course here, you have much more than the needed level of knowledge.
Course 1 of 5 in the Deep Learning Specialization.
Introduction to deep learning
Be able to explain the major trends driving the rise of deep learning, and understand where and how it is applied today.
Graded: Introduction to deep learning
Neural Networks Basics
Learn to set up a machine learning problem with a neural network mindset. Learn to use vectorization to speed up your models.
Graded: Neural Network Basics
Graded: Logistic Regression with a Neural Network mindset
Shallow neural networks
Learn to build a neural network with one hidden layer, using forward propagation and backpropagation.
Graded: Shallow Neural Networks
Graded: Planar data classification with a hidden layer
Deep Neural Networks
Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision.
Graded: Key concepts on Deep Neural Networks
Graded: Building your deep neural network: Step by Step
Graded: Deep Neural Network Application
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