Artificial intelligence can change your career by 180 degrees! 📌

Here's how you can start with AI engineering with zero experience!

The simplest definition of artificial intelligence|

Artificial intelligence (AI) is a part of computer science that creates smart systems to solve problems usually needing human intelligence.

AI includes tasks like recognizing objects and patterns, understanding voices, making predictions, and more.

Step 1: Master the prerequisites

Basics of programming
Probability and statistics essentials
Data structures
Data analysis essentials

Step 2: Get into machine learning and deep learning

Basics of data science, an intersection field
Feature engineering and machine learning
Neural networks and deep learning
Scikit-learn for machine learning along with Numpy, Pandas and matplotlib
TensorFlow, Keras and PyTorch for deep learning

Step 3: Exploring Generative Adversarial Networks (GANs)

Learn GAN fundamentals: Understand the theory behind GANs, including how the generator and discriminator work together to produce realistic data.

Hands-on projects: Build and train simple GANs using PyTorch or TensorFlow to generate images, enhance resolution, or perform style transfer.

Step 4: Get into Transformers architecture

Grasp the basics: Study the Transformer architecture's key concepts, including attention mechanisms, positional encodings, and the encoder-decoder structure.
Implementations: Use libraries like Hugging Face’s Transformers to experiment with different Transformer models, such as GPT and BERT, on NLP tasks.

Step 5: Working with Pre-trained Large Language Models

Utilize existing models: Learn how to leverage pre-trained models from libraries like Hugging Face to perform tasks like text generation, translation, and sentiment analysis.

Fine-tuning techniques: Explore strategies for fine-tuning these models on domain-specific datasets to improve performance and relevance.

Step 6: Introduction to LangChain

Understand LangChain: Familiarize yourself with LangChain, a framework designed to build applications that combine language models with external knowledge and capabilities.

Build applications: Use LangChain to develop applications that interactively use language models to process and generate information based on user queries or tasks.

Step 7: Leveraging Vector Databases

Basics of vector databases: Understand what vector databases are and why they are crucial for managing high-dimensional data typically used in AI models.
Tools and technologies: Learn to use vector databases like Milvus, Pinecone, or Weaviate, which are optimized for fast similarity search and efficient handling of vector embeddings.
Practical application: Integrate vector databases into your projects for enhanced search functionalities

Step 8: Exploration of Retrieval-Augmented Generation (RAG)

Learn the RAG approach: Understand how RAG models combine the power of retrieval (extracting information from a large database) with generative models to enhance the quality and relevance of the outputs.

Practical applications: Study case studies or research papers that showcase the use of RAG in real-world applications.

Step 9: Deployment of AI Projects

Deployment tools: Learn to use tools like Docker for containerization, Kubernetes for orchestration, and cloud services (AWS, Azure, Google Cloud) for deploying models.

Monitoring and maintenance: Understand the importance of monitoring AI systems post-deployment and how to use tools like Prometheus, Grafana, and Elastic Stack for performance tracking and logging.

Step 10: Keep building

Implement Projects and Gain Practical Experience

Work on diverse projects: Apply your knowledge to solve problems across different domains using AI, such as natural language processing, computer vision, and speech recognition.

Contribute to open-source: Participate in AI projects and contribute to open-source communities to gain experience and collaborate with others.

Hope this helps you ☺️

Google Gemini Unleashed
Natenapis Faraksa, 2024

HOW TO AVOID YOUR ADULT PROBELMS??

• Wake up early.
• Work out regularly.
• Eat good, real food.
• Live below your means.
• Find real friends with similar goals.
• Have more than 1 source of income.
• Do what you love for work
• Don't get into meaningless relationships.
• Stop hitting the snooze button.
• Create a routine.
• Write down a plan.

https://t.iss.one/HealthFitnessGymTips

The most popular programming languages:

1. Python
2. TypeScript
3. JavaScript
4. C#
5. HTML
6. Rust
7. C++
8. C
9. Go
10. Lua
11. Kotlin
12. Java
13. Swift
14. Jupyter Notebook
15. Shell
16. CSS
17. GDScript
18. Solidity
19. Vue
20. PHP
21. Dart
22. Ruby
23. Objective-C
24. PowerShell
25. Scala

According to the GitHub Repositories