Deep Learning: Visual Exploration
4.2 (185 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
11,525 students enrolled

Deep Learning: Visual Exploration

Deep neural networks visually explained in plain english & without complex math
4.2 (185 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
11,525 students enrolled
Created by Vladimir Grankin
Last updated 4/2018
English
English [Auto]
Price: $29.99
30-Day Money-Back Guarantee
This course includes
  • 4 hours on-demand video
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Deep understanding of what is deep neural network and how exactly it works under the hood to come up with good predictions in real life problems (we will only explore feedforward deep neural network for binary classification in our course, but we discuss fundamentals so knowledge you will get is also applicable to all the other network types!)
  • Understand what is decision boundary and how exactly it is formed by a deep neural network
  • Understand why deep neural networks are also knows as function approximators
Course content
Expand 15 lectures 04:01:33
+ Deep Learning - Deep Understanding by Visual Exploration
15 lectures 04:01:33

In this video I will provide you some links to good materials you may need in order to get up and running with deep learning.

Preview 06:54

High altitude overview of what we will do in this course.

What We Will Do
01:45

Here I will guide you through famous tensorflow playground platform which allows us to create simple deep neural networks and visually see how they work.

Preview 15:46

In order to train our neural network we first need data set to work with. In this video we will see data we will work with.

Preparing Data
11:42

In this video we will create a simple so called "feedforward" deep neural network for binary classification. This will be a network we will use throughout the course.

Preview 21:39

In this video we will see what is activation function and why it is used by a neural network. In particular we will explore sigmoid function, every single aspect of it.

Visual Exploration - Sigmoid Function (Activation Function)
38:02

In this video we will train our neural network using simple trick  instead of backpropagation in order to avoid complex math and make our experiments easier for the beginner.

Finding Parameters (Training our Deep Neural Network)
11:33

In this video we will see how input data is travelling through neural network, from start to end, in 2D.

Visual Exploration - Signal Journey Through Deep Neural Network (2D)
27:03

In this video I will briefly introduce you to backpropagation. We will discuss what it is and what it does.

Backpropagation Intro
15:00

In this video we will continue our journey on how input signal is transformed as it goes from neural network's input to it's output. This time in 3D.

Visual Exploration - Signal Journey Through Deep Neural Network (3D)
16:13

In previous videos we visualized data transformation. But data is transformed according to functions used in neurons. So in this video we will visualize functions themselves!

Visual Exploration - Visualizing Neurons of Our Deep Neural Network
30:49

In this video we will see what is the magic behind deep neural network. We will visually see why and how neural network is able to make accurate predictions using. We will use 2D plots.

Visual Exploration - Slices 2D (Neural Network's Magic Behind Good Predictions)
15:54

In this video we will continue to explore the magic behind deep neural network. In 3D this time.

Visual Exploration - Slices 3D (How Exactly Neural Network Makes Predictions)
13:32

In this video you will see how to visualize decision boundary for your neural network (just like the decision boundary you see in tensorflow playground neural network constructor).

Visual Exploration - Decision Boundary (How Deep Neural Network Classifies Data)
06:50

Summary and ideas for you to try at home.

Summary & Homework
08:51
Requirements
  • Basic python skills - optional, you will need python if you want to run the code we discuss yourself
  • Familiarity with neural networks at high level (terms like bias, weight and activation function should be familiar)
  • Jupyter notebook (optional and is needed if you want to run all the demos yourself)
Description

Visual introduction to Deep Learning based on simple deep neural network. Take this course if you want to understand the magic behind deep neural networks and to get a excellent visual intuition on what is happening under the hood when data is travelling through the network and ends up as a prediction at it's output.

In this course we will fully demystify such concepts as weights, biases and activation functions. You will visually see what exactly they are doing and how neural network uses these components to come up with accurate predictions.

Who this course is for:
  • People who like to understand things visually
  • People who like simple explanations against mathematical and formal ones
  • If you are just starting with Deep Learning or AI in general, this course if for you!
  • If you think what is happening under the hood of deep neural network is a mystery, this course is for you! - we totally demystify DNNs in this course!
  • If you wonder how exactly weights, biases and activation functions are working, this course is for you!
  • Experienced deep learning users who want to improve their understanding on how exactly Deep Neural network is able to come up with complex function approximations