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CourseDeep LearningLast updated 4 hours ago

Learn Deep Learning: From Beginner to Expert

This 28-part course consists of tutorials on deep learning concepts and neural networks, as well as quizzes and hands-on projects to practice implementing the algorithms and applying them to problems. You can think of this course as a "Free Online Nano Book".

Why Deep Learning?: Deep Learning has caused the revival of artificial intelligence. It has become the dominant method for speech recognition (allowing us to talk to the Google Assistant), computer vision (allowing us to search for "my pictures on the beach" on Google Photos), language translation and even game artificial intelligence (think AlphaGo and DeepMind). If you'd like to learn how these systems work and maybe make your own, deep learning is for you.

The primary objectives of this course are as follows:

Understand the core concepts in machine learning - model parameters, optimization, generalization, regularization, and so on.
Understand what deep learning is, and how deep learning differs from and relates to machine learning.
Understand what neural networks are and how they are trained using back-propagation. (and train your own neural network).
Understand the concept of computational graphs, a core idea (often overlooked in DL courses) foundational to understanding and implementing all sorts of complex neural network architectures.
Understand how Convolutional Nets work and how to solve computer vision tasks like image classification.
Understand how Recurrent Neural Nets work and how to generate text and perform language translation.
Bonus: Understand Generative Adversarial Networks, and how they can be used for generating images!

For more information such as expected completion time, pre-requisites, etc, see my reply at the bottom of the page. Subscribe to add this course to the top of your Home Page. Get started with the first article below.

An Introduction to Machine Learning

In this section, we'll learn about what machine learning is, and in particular, how it differs from general programming. We'll learn a specific machine learning model, linear regression, and see how the model can be trained using gradient descent. Gradient descent is also the how neural networks are trained, and in many ways, our linear regression model can be looked at as a very simple neural network.

We'll also learn about different types of problems in machine learning (supervised vs unsupervised), and talk about core concepts such as overfitting and regularization. This section ends with a beautiful visual recap of machine learning.

An Introduction to Deep Learning and Neural Networks

In this section, we'll learn about how deep learning differs from machine learning, i.e. what deep means and why it is important. We'll learn specifically what neural networks look like and how they are trained using back-propagation. We'll also introduce the concept of computational graphs which is how neural networks are implemented in popular deep learning libraries such as TensorFlow, Torch, etc and then train our first neural network. We'll end the section with learning regularization techniques specific to deep learning, such as early-stopping and dropout.

Convolutional Neural Networks and Computer Vision

We'll start by introducing the image classification problem in computer vision and a small note on how images are stored in a computer. Thereafter, we'll describe the architecture of a Convolutional Neural Network (CNN) in substantial detail, and see why the convolutional layer is very well suited for computer vision applications. Then, we'll solve the image classification problem using CNNs, and perform an ablation study which will allow us to improve our accuracy.

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What is Machine Learning? Why Machine Learning?

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An Introduction to Machine Learning

An Introduction to Deep Learning and Neural Networks

Convolutional Neural Networks and Computer Vision

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