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Learn Deep Learning: From Beginner to Expert

1654 students enrolled

This course consists of:

16 Tutorials

2 Projects

6 Quizzes

Certificate of Completion

This 24-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 what machine learning is, and learn the gradient descent algorithm.
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!

Prerequisites: Python, Linear Algebra, Statistics and NumPy and Calculus 1 (differentiation and chain rule).

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Syllabus

Machine Learning and Gradient Descent

We'll start by describing what machine learning is, and introduce a simple learning algorithm: linear regression + gradient descent. Using this algorithm, we'll introduce the core concepts in machine learning: model parameters, cost function, optimization method, and overfitting and regularization.

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.

What is deep learning? How does it relate to machine learning?
5 mins
A First Look at Neural Networks
9 mins
Quiz: Deep Learning and Neural NetworksPro
6 mins
Computational Graphs and Backpropagation
11 mins
Hands-on Assignment: Training your first Neural NetworkPro
7 mins
Quiz: The Importance of "Good" GradientsPro
9 mins
Regularization methods in Deep Learning
5 mins
Dropout (neural network regularization)
7 mins

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.

Long Short-Term Memory Networks and Natural Language Processing

We'll start by introducing Recurrent Neural Networks (RNN), which are neural networks with loops in them. We'll see how RNNs can be used for inputting and outputting sequences and how they maintain an internal state. Then we'll introduce Long Short-Term Memory (LSTM) networks and see how they overcome some significant shortcomings of general RNNs. Along the way, we'll also introduce the concept of word embeddings. We'll implement an RNN to generate text one character at a time, and also see how this model can be used for performing state-of-the-art language translation.

Recurrent Neural Networks and Long Short-Term Memory Networks
16 mins
Deep Natural Language Processing
20 mins

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What is Machine Learning? Why Machine Learning?

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Prerequisites

Python, Linear Algebra, Statistics, Calculus 1 (differentiation and chain rule) and NumPy.

Expected Completion Time

Printed out (textbook style, font, etc), these tutorials would be about 60-70 pages and non-stop reading time would be about 4-5 hours. 2 weeks if you're making progress everyday, or 3-4 weeks if you make progress regularly but not everyday.

Frequently Asked Questions

What does it cost?

Every tutorial on Commonlounge is available for free, and will always be. We only ask you to come back and contribute and make Commonlounge better for everyone once you are an expert. We offer premium content like quizzes, hands-on projects and certificate of completion as part of our Pro Plan. We also offer one-on-one mentorship and feedback on real-world projects from industry-experts as part of our Plus Plan.

When can I start?

Right now. Enroll in this course and follow it at your own pace. You can also set a reminder above to send the next item to your inbox every week.