What are good books to learn and implement AI?

[Ayush_]

Would appreciate your suggestions in which book or books should I read to gain knowledge and be able to implement artificial intelligence.

 

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[Andy Black]

Would appreciate your suggestions in which book or books should I read to gain knowledge and be able to implement artificial intelligence.

If you want to implement AI then i suggest forgetting books and courses and get stuck in.

Signup to ChatGPT and have a play with it. Check out YouTube videos showing how to use Copy.ai, Jasper.ai, etc.

There's been lots of YouTube videos uploaded recently about AI and ChatGPT in particular.

There's a few threads in the forum discussing AI and ChatGPT too.

 

[Here]

I am aware this isn’t a book, but it’s probably a good place to start.

Resources

Our resource center to help you get started and level up your skills as an AI practitioner | eBooks, Guides, Course Slides, AI Notes, and more.

www.deeplearning.ai

 

[Andreas Thiel]

What do you mean when you say "implement AI"? Do you have a goal or vision?

If you are talking about programming, then I would start with researching online to get a feel for what is out there, then buy books once you have identified areas where a deep dive makes sense.

A good type of resource is always the "awesome" github repositories that collect links to interesting projects and articles.

GitHub - amusi/awesome-ai-awesomeness: A curated list of awesome awesomeness about artificial intelligence

A curated list of awesome awesomeness about artificial intelligence - amusi/awesome-ai-awesomeness

github.com

GitHub - jtoy/awesome-tensorflow: TensorFlow - A curated list of dedicated resources http://tensorflow.org

TensorFlow - A curated list of dedicated resources http://tensorflow.org - jtoy/awesome-tensorflow

github.com

GitHub - keon/awesome-nlp: :book: A curated list of resources dedicated to Natural Language Processing (NLP)

:book: A curated list of resources dedicated to Natural Language Processing (NLP) - keon/awesome-nlp

github.com

GitHub - josephmisiti/awesome-machine-learning: A curated list of awesome Machine Learning frameworks, libraries and software.

A curated list of awesome Machine Learning frameworks, libraries and software. - josephmisiti/awesome-machine-learning

github.com

GitHub - awesomedata/awesome-public-datasets: A topic-centric list of HQ open datasets.

A topic-centric list of HQ open datasets. Contribute to awesomedata/awesome-public-datasets development by creating an account on GitHub.

github.com

...

 

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[Ayush_]

What do you mean when you say "implement AI"? Do you have a goal or vision?

If you are talking about programming, then I would start with researching online to get a feel for what is out there, then buy books once you have identified areas where a deep dive makes sense.

A good type of resource is always the "awesome" github repositories that collect links to interesting projects and articles.

GitHub - amusi/awesome-ai-awesomeness: A curated list of awesome awesomeness about artificial intelligence

A curated list of awesome awesomeness about artificial intelligence - amusi/awesome-ai-awesomeness

github.com

GitHub - jtoy/awesome-tensorflow: TensorFlow - A curated list of dedicated resources http://tensorflow.org

TensorFlow - A curated list of dedicated resources http://tensorflow.org - jtoy/awesome-tensorflow

github.com

GitHub - keon/awesome-nlp: :book: A curated list of resources dedicated to Natural Language Processing (NLP)

:book: A curated list of resources dedicated to Natural Language Processing (NLP) - keon/awesome-nlp

github.com

GitHub - josephmisiti/awesome-machine-learning: A curated list of awesome Machine Learning frameworks, libraries and software.

A curated list of awesome Machine Learning frameworks, libraries and software. - josephmisiti/awesome-machine-learning

github.com

GitHub - awesomedata/awesome-public-datasets: A topic-centric list of HQ open datasets.

A topic-centric list of HQ open datasets. Contribute to awesomedata/awesome-public-datasets development by creating an account on GitHub.

github.com

...

By implementation i mean code

 

[Andreas Thiel]

By implementation i mean code

Do you already have a product idea in mind (image recognition? Chatbot? Autonomous robots?), or do just want to enter the field with an open mind and experiment?

 

[Ayush_]

Do you already have a product idea in mind (image recognition? Chatbot? Autonomous robots?), or do just want to enter the field with an open mind and experiment?

I don't have a particular idea right now I just want to experiment atm.

 

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[Andreas Thiel]

There is a Twitter thread where people post links to resources in the comments:

https://twitter.com/i/web/status/1614063428649340930

View: https://twitter.com/PeterDiamandis/status/1614063428649340930

 

[S.Y.]

If you want to implement AI then i suggest forgetting books and courses and get stuck in.

Signup to ChatGPT and have a play with it. Check out YouTube videos showing how to use Copy.ai, Jasper.ai, etc.

There's been lots of YouTube videos uploaded recently about AI and ChatGPT in particular.

There's a few threads in the forum discussing AI and ChatGPT too.

Am a big fan of learning by doing, and I disagree with you.

The jury is still out and only time will tell, but if anything ChatGPT can end up being a disaster and extremely biased. I am afraid many people think they understand machine learning because of the apparent ease of using chatGPT. And that can lead to dangerous and nefarious long-term consequences.

I suggest reading: ChatGPT: Optimizing Language Models for Dialogue. They explain how they "train" the model. They openly share the downside.

What I suggest:
1. Get a broad overview of the machine learning field. What is referred to "AI" is quite broad.
2. Pick one field you are interested in pursuing and which angle you are taking. Do you want to be coding/deploying yourself, or do you want to be more of the visionary, thinking of ways to solve problems with AI and leading a team of experts
3. Go on Kaggle and see how it is being used to solve real-world problems
4. Decide what you need to learn and go after it. This is where getting practical help. Do projects, a lot of them.

 

[Andreas Thiel]

View: https://youtu.be/VMj-3S1tku0


Andrej Karpathy has great introduction videos. They should give an idea of the kind of math that is involved and what the code of basic projects looks like.

 

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