Deep Learning, AI/ML, Computer Vision, and Data Science Gift Buying Guide - 2019

It’s the holiday season! This is the season of the year we like to celebrate the most. And it is the part of the year that we'd like to give, and receive, gifts.

While it's easy to pick a gift (relatively speaking) for some people, the interests of some others might make it hard nail down a gift for. (Take an AI, ML, Data Science and programming enthusiast like me for an example)

Now, you might have a family member, colleague, or a friend that is into AI, Deep Learning, and Machine Learning. Or, they might be working on or interested in Data Science or Computer Vision. You might be wondering what sort of gifts to get them.

Or simply as an enthusiast in those areas yourself, you're thinking of buying a gift for yourself this season. (I know that I’m thinking the same)

So, how do you select a gift for someone with interest in such vast and technical fields?

Well today I’m going to give you some gift ideas that just might work.

When thinking of gifts relating to the AI/ML, CV, Data Science fields, we can consider three categories of gifts,

1. Items to Improve their ability to perform tasks in those areas
2. Give them new tech toys to play around in those areas
3. Help them improve their knowledge in those areas

Let’s see what items we can select for each of those categories.

Build Deeper: Deep Learning Beginners' Guide

I've been away from writing a post for about three weeks. That's because I've been preparing something exciting.

Today, I'm happy to announce the first book release from Codes of Interest - Build Deeper: Deep Learning Beginners' Guide.

Build Deeper: Deep Learning Beginners' Guide

Deep Learning has become a household name. It’s the bleeding edge in AI, and already achieving some phenomenal feats. Breakthroughs are happening daily, and the tech giants are not only pursuing it, they’re leading it.

Build Deeper: Deep Learning Beginners' Guide is the ultimate guide for anyone taking their first step into Deep Learning. Learn what Deep Learning is, and how it came to be. See what it's capable of, and its milestones. And get hands-on with building your first Deep Learning model.

All you need to get started is a bit of enthusiasm, and some basic programming skills.

Build Deeper: Deep Learning Beginners' Guide is now available from Amazon.

Get your copy now!

What is Deep Learning? - Updated

What is Deep Learning? And, how does it relates to Machine Learning, and Artificial Intelligence?

I did an article to answer these questions some time back.

Now, thanks to the feedback I got from you all, I was able to updated it, with more clarifications, improved examples, and answers to more questions in Deep Learning.

Check out the updated article here,

What is Deep Learning?

Your feedback are always welcome.

Build Deeper: Deep Learning Beginners' Guide is the ultimate guide for anyone taking their first step into Deep Learning.

Get your copy now!

How deep should it be to be called Deep Learning?

If you remember, some time back, I made an article on What is Deep Learning?, in which I explored the confusion that many have on terms Artificial Intelligence, Machine Learning, and Deep Learning. We talked about how those terms relate to each other: how the drive to build an intelligent machine started the field of Artificial Intelligence, when building an intelligence from scratch proved too ambitious, how the field evolved into Machine Learning, and with the expansion of both the capabilities of computer hardware and our understanding of the natural brain, dawned the field of Deep Learning

We learned that the deeper and more complex models (compared to traditional models) of Deep Learning are able to consume massive amounts of data, and able to learn complex features by Hierarchical Feature Learning through multiple layers of abstraction. We saw that Deep Learning algorithms don’t have a "plateau in performance" compared to traditional machine learning algorithms: that they don’t have a limit on the amount of data they can ingest. Simply, the more data they are given, the better they would perform.

The Plateau in Performance in Traditional vs. Deep Learning

With the capabilities of Deep Learning grasped, there’s one question that usually comes up when one first learns about Deep Learning:

If we say that deeper and more complex models gives Deep Learning models the capabilities to surpass even human capabilities, then how deep a machine learning model should be to be considered a Deep Learning model?

I’ve had the same question when I was first getting started with Deep Learning, and I had few other Deep Learning enthusiasts asking me the same question.

It turns out, we were asking the wrong question. We need to look at Deep Learning from a different angle to understand it.

Let’s take a step back and see how a Deep Learning model works.

New Page - What is Deep Learning?

Deep Learning is the latest trend in Machine Learning. We hear about new innovations in Deep Learning every day, to a point that it has started to become a household name. Big companies such as Google, Apple, Amazon, Microsoft, IBM, and many others seems to be pushing for Deep Learning.

But, do you know what Deep Learning really is?

Since it's one of the most frequent questions that comes up, I created a new page "What is Deep Learning?" which talks about what Deep Learning is, how it relates to Machine Learning and Artificial Intelligence, and their history. Have a look at it from the below link,

What is Deep Learning?

Let me know what you think of Deep Learning.

Build Deeper: Deep Learning Beginners' Guide is the ultimate guide for anyone taking their first step into Deep Learning.

Get your copy now!

Difference between Artificial Intelligence, Machine Learning, and Deep Learning

Update: Check out the new and updated article on What is Deep Learning, and how it relates to Artificial Intelligence and Machine Learning.

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You may have heard the terms Artificial Intelligence, Machine Learning, Deep Learning and you maybe trying to figure out what they mean, and whether these terms can be used interchangeably.

I've also had the same questions when I started diving in to the field. And a recent post in the Nvidia Blog brought back the question.

So here’s a simplified explanation on how each of those terms came to be, and how they relate to each other.

Artificial Intelligence

 

Artificial Intelligence is the idea that machines (or computers) can be built that has intelligence parallel (or greater) to that of a human, giving them capability to perform tasks that requires human intelligence to perform.

The idea of an intelligent machine has been around since 1300 BC, and through 19th century. But the Dartmouth Conferences in 1956 is what’s commonly considered as the starting point of the formal research field of Artificial Intelligence. Since then the field of AI has gone through many ups-and-downs and has branched out into many sub fields. There has been attempts at applying AI for various fields – such as medical, finance, aviation, machinery etc. – with various degrees of success.

Around the late 1990s and early 2000s, the researchers identified a problem in their approach to AI, which was slowing down the success of AI – in order for us to artificially crate a machine with an intelligence, we would first need to understand how intelligence work. But even today, we do not have a complete definition of what we call "intelligence".

In order to tackle the problem, they decided to go ground-up – rather than trying to build an intelligence, we could look in to building a system that can grow its own intelligence. This idea created the new sub-field of AI called Machine Learning.