5 Resources for Lifelong A.I. Learners
You read, you learn, you grow!
Artificial Intelligence is everywhere these days. A lot of real life applications have been developed in the past decade leveraging various sub-branches of A.I. Facial Recognition, Self Driving Cars, Optical Character Recognition, Personal Assistants like Google Home and what not.
And we are still just scratching the surface. A lot of new mind-blowing researches are still in progress and people are trying really hard to find novel applications for these technologies. In the coming decades, we might completely take for granted the cutting edge A.I. tech today(thanks to the exponential growth)
For an average software engineer, these advancements are just an API call away from being used in their applications. For example, Google provides Vision API to extract objects/texts from an image. These API’s cover most of the basic usecases, but for someone with a more specific use case, transfer learning can be used to train these generic models to provide more accuracy. Nanonets is one such service that makes this really easy.
Now, for some of us, with curious and nerdy minds, we like to know what’s under the hood and how can a computer program learn to drive a car or recognise characters or respond to random questions (almost)like a human. I wanted to share some of my resources for understanding this magic that goes on behind these cool developments.
1. Deep Learning by Ian Goodfellow
This book is one of the best resources to enter the field of Deep Learning. It begins with an introduction to the mathematical topics like Linear Algebra, Probability and Statistics, Information Theory etc.
Then it continues to explore various topics in Machine Learning, what were the problems and how Deep Learning came to the rescue.
This books walks you through the concepts required to get started in Deep Learning and then moves on to specific topics like Feed Forwards Nets, Convolutional Nets, Recurrent Nets etc.
The authors, Ian Goodfellow, Yoshua Bengio, Aaron Courville have compiled a great piece for beginners and intermediate level learners. Yoshua Bengio is one of 3 pioneers of 21st centuries Deep Learning advancements.
This book is a must have weapon in you arsenal and its also available for free at deeplearningbook.org. But if you prefer a hardcopy like me get it here.
2. Pattern Recognition and Machine Learning by Christopher Bishop
Pattern Recognition is an integral part of Intelligent Machines, it is what enables you to recognise patterns or regularities in data and create intelligent models, able to perform predictions, classifications and much more.
This books focuses on getting you aquainted with those techniques and understand them in depth. You’ll get to learn how to decide which techniques to use for the dataset in hand and how to make the most of our data. It covers topics like regression, classification, neural networks in depth.
You’ll go through a lot topics related to statistics and probabilty as well. Given the direction that AI reserch is headed today, these skills are going to be of utmost importance.
3. brilliant.org
is a brilliant initiative towards STEM learning. Their moto is to educate people about STEM topics in very interesting and engaging ways.
They use Q/A technique to engage the user with their carefully curated courses, designed to help you form a deep and lasting understanding of the topics. Their crew of content creators include scientists, professors from renowned universities, people themselves practitioners of lifelong learning.
Their course catalog includes various courses related to mathematics and artificial intelligence. And they constantly add new courses related to artificial intelligence and computer science.
They also bring you new interesting problems everyday to solve and keep the heavy machinery running. Their yearly subscription costs $120 but I suggest that you try out a monthly subscription first.
4. Foundation of Statistical Natural Language Processing by Christopher Manning
One of my own personal favourite among all A.I. branches is Natutal Language Processing. Ever since I saw Iron Man, I have been fascinated by the program Jarvis and how he keeps Mr. Stark company.
I feel that NLP is going to be a very important part to the future advancements in the field of A.I. There is a lot of research active in NLP amoung various tech leaders like Google, Open AI.
This book covers Statistical approaches to NLP from the very basics including mathematics and linguistics. It is a lengthy textbook, but nonetheless very important one if you really want to do solid research in NLP.
The author, Christopher D. Manning, Professor of Computer Science and Linguistics at Stanford University with an h-index of 122, not only takes you through statistical bases for NLP, but also studies language, to understand reasons for why 2 words occur together or how new trends in language are formed over the years.
I’m currently halfway through the book and it has been a great resource.
3Blue1Brown
I prefer to read rather than watch a video to learn something. But sometimes a visual stimuli is all you need to understand that topic which you are struggling to comprehend.
Most of you might have already heard about this youtube channel, it has some amazing videos on various mathematics topics including Linear Algebra and Calculus.
I have seen some videos on Linear Algebra on this channel and the way they explain it is really amazing. With the help of animations in the videos, you can easily understand any topic and get a better understanding about what goes on when we perform certain operations.
For some concepts visualisation is the key, if reading texts doesn’t help visualize a concept, watching a video about it might fit in the missing peice of block hindering your understanding.
They have even started a lockdown series to go over some basic mathematical concepts, make sure you check them out as well.
Conclusion
These were some of my own favourite resources I use to understand behind the scenes of various complex Artificial Intelligence concepts. Hope you’ll find atleast one of them usefull.
Let me know your top resources for studying AI in comments!
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