Generative AI
A Road Map to AI Learning by 2024
You would like to learn AI, then? But you're not sure where to begin or how to proceed? The significance of artificial intelligence (AI) is becoming more and more apparent as we live at what may be an important moment in the evolution of humanity.
Furthermore, programs like Bard, Midjourney, and ChatGPT are bringing AI into the general public. Because of this, AI as an art and science are more important than ever.
In addition to going over the knowledge and resources you'll need, we also look at how firms might use AI in the modern business environment.
What is artificial intelligence (AI)?
The goal of the computer science field known as artificial intelligence, or AI, is to build machines that are capable of carrying out tasks that usually call for human intelligence.
AI is a large field with several smaller subdivisions, each with specific goals and areas of expertise. Visit our comprehensive guide, What is AI?, to learn more.
Some of the world's top universities, including Harvard, Google, Amazon, and DeepLearning, are where you will acquire all of these skills. AI without charge.
What are types of artificial intelligence?
AI has been brought up in a variety of ways as it becomes more and more mainstream. Based on its capacities, artificial intelligence can be divided into three levels:
Today, the most prevalent type of artificial intelligence that we deal with is artificial narrow intelligence (ANI). Like voice recognition or suggestions on streaming services, ANI is created to do a single task.
General Intelligence Artificial (AGI): An AI that has artificial general intelligence (AGI) can comprehend, learn, adapt, and apply knowledge to a variety of activities at a human level.
Artificial Super Intelligence (ASI): The ultimate form of AI, ASI describes a state of affairs where AI would eventually outperform human intelligence in almost all profitable tasks. Though intriguing, this idea is still mostly conjectural.
What Makes Artificial Intelligence Important to Learn Now?
Artificial intelligence is a breakthrough technology that is changing the way we live, work, and interact. It is not simply a catchphrase.
The need for AI specialists is expected to rise as more businesses use AI technologies to improve decision-making and streamline operations.
The anticipated expansion in the size of the AI market between 2021 and 2030 emphasizes this projection even more.
AI is a lucrative field.
Of course, the increased demand for AI expertise translates into competitive pay. As of November 2023, the average annual compensation for an AI engineer in the United States is $153,719, with the possibility of incentives and profit-sharing, according to data from Glassdoor.
The average annual salary for machine learning engineers as well as data scientists is $151,158 and $178,515, respectively. The worth and influence of AI abilities in the job market are reflected in this monetary compensation.
Artificial intelligence is difficult to understand.
There is more to artificial intelligence than just well-paying professions and huge market demand. Intriguing challenges await you in this field, which is also intellectually stimulating.
It entails creating models that mimic human intellect, developing methods to tackle complicated issues, and developing innovative applications.
AI experts are always learning, evolving, and adapting. Since the subject is always changing, there are always new concepts to grasp, issues to resolve, and frameworks to get better at.
How Much Time Is Needed to Learn AI?
In the coming years, there will be an enormous increase in demand for AI specialists.
New roles such as prompt engineer, AI consultant, and AI engineer will appear as businesses start integrating AI models into their procedures.
These are well-paying careers, with average yearly incomes of $136,000 to $375,000.
Furthermore, there has never been a better moment to join the workforce with AI expertise, as this field is only now beginning to gain widespread recognition.
But the field of artificial intelligence has far too much to learn.
It can seem impossible to keep up with these changes and pick up new technologies at such a rapid pace when there are new developments in the industry almost every day.
Thankfully, you are not required to.
Entering the field of artificial intelligence does not require knowledge of every new technology.
All you need to know to create AI solutions for any use case is a basic understanding of a few principles.
Some of the top universities in the world, including Harvard, Google, Amazon, and DeepLearning, are where you will acquire all of these skills. AI without charge.
How Will AI Be Learned From Scratch in 2024?
Mathematics:
The foundational concepts of AI algorithms include statistics, calculus, probability, and linear algebra.
Sources: Weights & Biases-Based Math for Machine Learning (code) Fast.AI's Computational Linear Algebra (video, code) An Overview of Linear Algebra for Utilising Python in Applied Machine Learning Lectures at Imperial College London on Multivariate Calculus and Linear Algebra 3Blue1Brown: Essentials of Calculus Statistics and Linear Algebra by StatQuest
Tools:
Python:
Take up new talents or learn the fundamentals. Sources: Hands-on Python Coding Mastery of Advanced Python by David Beazley Speeches by James Powell Book: Second Edition of Fluent Python (code) Podcasts: Talk & Real Python
PyTorch:
A framework for deep learning. Sources: Aladdin Persson's PyTorch Tutorials Official Tutorials & Examples for PyTorch Exercise: Tensor/srush puzzles Book: Deep Learning with PyTorch Programming
Machine Learning
Create something from scratch.
Write the algorithms from scratch as you read.
Examine the following repositories.
Create algorithms from the ground up. Sources: Depositories: ML-From-Scratch and eriklindernoren Aaron T. Leb/homemade machine learning, and Jeremy Nixon/oracle A Do-It-Yourself Machine Learning Engineering Course, MiniTorch (videos, code)
Join ML contests on Bitgrit, Kaggle, and other platforms.
Take on secondary projects: Create a model, then implement it. Resources: Vicki Boykis's earthaccess (NASA Earth data) streamlit (build user interface) machine learning in production.
Install models and monitor experiments. Sources: ML-Made ZoomCamp/DataTalksClub: Free MLOps course chiphuyen/design of machine learning systems Apparently, 300 case studies on AI—MML system design ml-engineering/stas00.
Deploy them
Start producing the models. Keep a record of your experiments. Study up on model monitoring. Get practical experience with data and model drift.
These are a few top-notch resources.
Deep Learning
To achieve top-down, begin fast.aI.
Books:
Explore Deep Learning with Code with PyTorch, NumPy/MXNet, Java, and TensorFlow Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book Deep Learning Deep learning and neural networks Comprehending Deep Learning Using Notepads Articles from The Little Book of Deep Learning: A Formula for Neural Network Training 33 Years Ago and 33 Years Ahead for Deep Neural Nets
Participate in the PlantTraits2024 FGVC11 (computer vision) deep learning challenge on Kaggle.
Use LabML.AI to implement research papers. Implementations of annotated PyTorch papers, code-exposed papers (e.g., BERT explained).
Participate in more contests:
PlantTraits2024—FFGVC11 | Kaggle (computer vision)
Additional:
Transformers Book: Natural Language Processing using
Large Language Models (LLMs):
Go through papers and watch videos:
[One-Hour Talk] Andrej's Introduction to Large Language Models GPT in 60 Lines of NumPy | Jay Mody—BBig Language Models in Five Formulas by Alexander Rush—CCornell Tech Neural Networks: Zero to Hero by Andrej Karpathy
Presenting Full Stack Deep Learning's Free LLM Bootcamp.
Create LLM apps with Andrew Ng's book, Application Development with Large Language Models: Construct LLM apps for Huyen's production. Eugene Yan's Chip Patterns for Developing LLM-based Systems & Products
Read Huyen Chip's Building LLM apps for manufacturing.
In addition, Eugene Yan's Patterns for Developing LLM-based Systems & Products.
Write Transformers from the ground up.
For a summary, read The Transformer Family Version 2.0 | Lil'Log.
Select your preferred format and create it from scratch.
RAG
An excellent piece from Anyscale: Developing RAG-based LLM Applications for Manufacturing
An in-depth analysis of Aman Chadha's retrieval-augmented generation
Conclusion
Learning artificial intelligence (AI) is a fulfilling endeavour that leads to a world of cutting-edge technologies and fascinating job prospects. This approach yields information and competence that goes above lectures and publications.
It entails a dynamic cycle of application, experimentation, learning, and improvement. Adopting a hands-on approach speeds up learning and fosters critical thinking, creativity, and problem-solving skills. This is especially true for courses and AI projects.
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