Mastering AI from scratch in five steps
Ultimate guide to become AI expert
In 2023, major companies have ignited a competition in artificial intelligence, leading to a skyrocketing demand for AI talents. The compensation in this field far exceeds that of other industries, with million-dollar annual salaries no longer a dream. Even for tech professionals in non-AI fields, it’s essential to start learning quickly to prevent falling behind. Today, I’m going to share with you how to transform from a beginner to an AI expert, so be sure to bookmark this and study it diligently!
Step 1: Learn Python.
Almost all AI and deep learning frameworks are based on Python. You must learn Python before you can proceed to work with AI frameworks. Personally, I find Python much easier to learn compared to languages like C++. The key is to grasp various concepts such as syntax, data structures, classes, modules, and more. There are numerous tutorials available, and I recommend two YouTube video tutorials: “CS Dojo” and “FreeCodeCamp.” I recently reviewed them myself. Even when you’re solving LeetCode problems, you can simply choose Python as your preferred language.
Step 2: Become familiar with deep learning frameworks
I recommend focusing on learning one of frameworks such as PyTorch, TensorFlow, Caffe, and more. Currently, I use PyTorch, mainly because most open-source AI projects are based on PyTorch. It has a user-friendly interface and integrates various recent AI libraries. I suggest watching the YouTube video “Deep Learning with PyTorch — Full Course” and checking out DataCamp’s “Introduction to PyTorch” to get started.
Step 3: Learn about the latest AI models and APIs.
It’s essential to be familiar with platforms like Hugging Face. Hugging Face offers a wealth of both classic and cutting-edge AI models (over 20,000!). Models like Bert, Llama, GPT, and many others are readily available for download and use. Hugging Face also provides many well-written tutorials that you can read and learn from.
Hugging Face Transformer offers a variety of AI models based on the Transformer architecture. What’s more, you can seamlessly integrate these models into frameworks like PyTorch, TensorFlow, and others. I’ve recently used Hugging Face to run GPT and Llama on Nvidia GPUs, and it works exceptionally well!
Step 4: Understand various deep learning acceleration libraries.
Different hardware companies have developed their own sets of acceleration libraries. For example, Nvidia has TensorRT and cuDNN, which are designed to accelerate AI workloads on GPUs. Intel offers OpenVINO to speed up AI on Intel CPUs and GPUs. As AI models continue to grow in size and complexity, these libraries are essential for improving computational speed. It’s crucial to have knowledge of these libraries in order to optimize AI model performance.
Step 5: Practice! Practice! Practice!
Finally, once you have learned the foundational knowledge mentioned above, you can start working on your own AI projects. Here are a few popular generative AI projects from this year that you can try:
- Build a chatbot with Llama2
- Query a document with Langchain
- Text summary with LLMs using hugging face
- Turn natural language into SQL with LlamaIndex
With experience in a few projects like these, you’ll be well on your way to standing out in the field of AI.
