Building a CNN on a GPU: A Beginner’s Guide

Artificial Intelligence has been gaining a lot of popularity these days and rightfully so. According to a study, 77% of companies are using AI in their businesses, and 83% of companies plan to explore AI in their upcoming business plans. This has led to an increase in the number of deep learning enthusiasts building their own convolutional neural networks or CNNs. 

And if you’re one such enthusiast, you’ve come to the right place! Deep learning may be complex, but we have narrowed it down for you in this beginners guide! Come join us as we explore the world of deep learning and how to build a CNN on a GPU. Let’s jump in!

What is a Convolutional Neural Network?

Convolutional Neural Networks are a subset of machine learning where they use three-dimensional data for image classification and object recognition tasks. They contain an input layer, along with other node layers, one or more hidden layers and an output layer. Each node has an associated threshold and weight and can be interconnected.

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If the output of any individual node ends up being greater than the associated threshold, the node is then activated. This in turn sends the data to other layers of the network. If this process does not take place, the data is not passed along the network. 

‍Steps To Build A CNN Using GPU Acceleration

1. Pre-requisites 

Building a CNN with the help of GPU acceleration might seem complex. But do not worry fellow enthusiastic learners, we’ve got it covered for you! 

Before jumping into the process of building, it is crucial to access if you have the prerequisites in order. These include:

A. Knowledge of R/Python

R and Python are the two most commonly used languages for learning. The main reason as to why they’re popular is because of how active both the communities are and how proactive they are with the support for the same. Before you start building, it is necessary to pick either of the two languages based on your knowledge and understanding of them.

B. Understanding Of Linear Algebra, Probability And Calculus

Since building your own CNN requires complexities, brushing up your math skills, specifically in linear algebra, probability and calculus, can be beneficial. 

C. Knowledge of neural networks and deep learning

Having an understanding of neural networks and machine learning can be significantly helpful in helping you build a CNN. Several courses are available online to help you brush up your knowledge on the same or teach you something entirely new. A notable one is Neural Networks And Deep Learning.

D. Set-up

Since building a CNN is heavily reliant on computational concepts, you would need machines that can handle heavy computations. Before you start your building process, ensure that you have the following:

• GPU Server (+4 GB, preferably PoolCompute)
• CPU ( eg: Intel i3 or above)
• 4 GB RAM (or depending on the dataset)

2. Technical Aspects and Concepts

The key to deep learning starts with neural networks. Logical implementation of these neural networks to abstract useful information from the data is essential to what deep learning is all about. Once you decide on what learning medium works best for you, you can get a better grasp of the intricacies of deep learning. 

Here are a couple blog posts and books that can help accelerate your learning:

Blogs:

Fundamentals of Deep Learning - Starting with Artificial Neural Network

A Deep Learning Tutorial: From Perceptron to Deep Networks

Books:

Neural networks and Deep Learning (A free book by Michael Neilson)

‍Deep Learning (An MIT Press book)

3. Find Your Technique

3. Find Your Technique 

Once you’ve got the basics covered, it is time for the fun part! Implementing your theoretical knowledge in practical projects. There are several techniques that can be used based on your need and interest. These include: 

A. Computer Vision / Pattern Recognition 

Given that pattern recognition is frequently a component of computer vision, there is not much difference between the two. In contrast to pattern recognition, which is not limited to images, computer vision, in its broadest sense, involves solely analyzing images and is utilized for tasks like object detection, segmentation, vision-based learning, etc. It has to do with categorizing anything that is capable of having a pattern.

B. Speech and Audio Recognition

Ever been intrigued by Alexa or Siri responding to you? It is due to a speech recognition feature that allows them to identify your requirements and search for the necessary accordingly. 

This technique consists of a neural network involving sequences of inputs to create cycles in the neural graph that are known as Recurrent Neural Networks (RNN). 

C. Natural Language Processing or NLP

C. Natural Language Processing or NLP

NLP is a way in which computers read, analyze and respond by imitating the human language is a smart and beneficial manner. The NPL layer converts user requests or inquiries into data and consults its database to find the appropriate answer. Translation across human languages is an advanced application of natural language processing (NLP).

D. Reinforcement Learning Or RL

Imagine if a robot could train itself from its previous actions and learn something new whenever needed? Sounds too good to be true, right?

Well it is quite a real concept!

A similar idea is introduced for computer agents through reinforcement learning, wherein the agent is rewarded or punished based on its actions on an object, regardless of its performance in a given task. As part of the deep learning model that governs its behavior, it learns more about it.

4. Choose the Right Framework

Ideally, for some deep learning tasks, traditional machine learning algorithms are beyond enough. But for tasks with heavy use of images, videos, text or speech, you might have to opt for a different framework.

However, most people often find it difficult to find the right framework for them. But there is no such thing as the right framework, only the suitable one for the task. 

Before you select a framework, ensure the following criterias are looked upon:

• Availability of pre-trained models
• Supported operating systems and platforms
• Open source
• Licensing Model
• Availability of debugging tools
• Ease of model definition and tuning
• Ease of extensibility (Can code new algorithms)

For better clarity, and to make choosing a framework easier, here a couple popular ones in the market currently:

1. TensorFlow

A subsidiary of Google, TensorFlow is an all-purpose deep learning tool and library for numerical computation based on data flow graph representation.

2. Theano 

Theano is a popular math expression compiler and an actively developed architecture that defines, optimses and evaluates mathematical expressions having multi-dimensional arrays. 

3. Microsoft Cognitive Toolkit

Microsoft Cognitive Toolkit, formerly known as CNTK, is a unified deep learning toolkit that can be used on numerous GPU Drivers and servers to easily realize and combine popular model types including CNN, RNN, LTSM, and more.

5. Explore Deep learning

Though deep learning is complex, one cannot deny that it is a prominent field of Artificial Intelligence. The three ley pointers that drives deep learning are:

• Availability of significant amounts of training data
• Advances in academia 
• Powerful computational infrastructure

Before you master the art of deep learning, ensure that you: 

• Repeat step 2- step 4, with a different technique each time
• Keep testing your skills consistently 
• Keep track of recent researches/researchers in the field

Frequently Asked Questions

1. How do I know if my GPU is working properly?

To ensure that your GPu is working properly, you can check the visual artifacts in games or run benchmarks.

2. What is the fastest AMD GPU?

The fastest AMD GPU depends on the benchmark. However, the Radeon RX 7900 XT is one of the most popular contenders. 

3. Which GPU is best for Deep Learning?

This can be best determined by your subjective needs. But GPU’s such as Nvidia GeForce RTX 30 series and PoolCompute GPu’s are in popular demand. 

4. How much RAM do I need for Deep Learning?

The amount of RAM needed for deep learning typically depends on your datasets. The usual starting point for advanced learning is 16 to 32 GB.

5. What is a reference design Graphics Card?

The base design supplied by the GPU manufacturer is referred to as a reference design graphics card; custom cards from vendors like ASUS or MSI may come with unique cooling systems or factory overclocking.