# Cut the bullshit - AI

>Created by @[Juan Blanco](https://twitter.com/juanilloblanco)

To enhance your practical understanding of AI and stay up to date with current advancements, I recommend the following learning path and [reccomendations](https://hackmd.io/3PWvfvi5SqWrpryGzgkRsA#Recommendation):

1. [**Pizza and AI**](https://www.youtube.com/watch?v=q6kJ71tEYqM)

2. **Fast.ai** - Visit their website at [Fast.ai](https://course.fast.ai/) and explore their courses, which are divided into two parts:
- Part 1: Review fundamental concepts.
- Part 2: Cover breakthroughs from recent years.

> If you prefer a more abstract and bottom-up approach, consider taking Deeplearning.ai courses instead of Fast.ai.

3. **Kaggle** - Engage with Kaggle, a popular platform for data science and machine learning competitions. Participate in challenges and explore the vast collection of datasets and kernels shared by the community.

4. **Huggingface** - Check out the resources provided by Huggingface at [Huggingface.co](https://huggingface.co/). Move fast and break things.

5. **Langchain** - Explore Langchain, a platform dedicated to language-related AI technologies. Learn about their offerings and stay updated with advancements in NLP.

5. **Mojo**, the new hot thingy.

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For a deeper understanding of deep learning concepts, consider taking courses that cover similar topics in a more abstract manner. These courses can serve as [valuable complements to your learning journey](https://www.deeplearning.ai/).

Infrastructure and MLOps
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Regarding the infrastructure and AI model lifecycle, various cloud providers offer their own courses and solutions. While the specifics may vary, the underlying principles and structures remain similar. You can explore resources such as:

- [Google Cloud AI Infrastructure](https://cloud.google.com/ai-infrastructure) and watch videos like [this one](https://www.youtube.com/watch?v=R0vC31OXt-g) to gain insights into building AI models and leveraging cloud infrastructure.

- [Machine Learning Engineering for Production (MLOps) Specialization](https://www.deeplearning.ai/courses/machine-learning-engineering-for-production-mlops/)
- [Practical Data Science on the AWS Cloud (PDS) Specialization](https://www.deeplearning.ai/courses/practical-data-science-specialization/)

To stay updated
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To stay updated and engaged with fun AI topics, consider following the Twitter account [Dair_ai](https://twitter.com/dair_ai) and exploring the YouTube channel [DavidShapiroAutomator](https://www.youtube.com/@DavidShapiroAutomator). These platforms provide regular updates and interesting discussions on AI-related subjects.

Finally, to expand your knowledge further, explore research papers on platforms like [arXiv](https://arxiv.org/abs/). These papers delve into the latest advancements and breakthroughs in the field of AI.

Recommendation
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When faced with unfamiliar concepts, it's beneficial to enhance your understanding by exploring supplementary courses available on platforms such as Coursera or YouTube.

However, avoid delving too deeply into each concept; instead, focus on getting it to work, and a project-oriented mindset -- Learn by doing -- Gradually accumulate knowledge over time to maximize your learning experience.

**HAVE FUN!**