LLM Cheatsheet

Machine Learning with Python - 2023
#python #en

Build Your Own Robot
Marwan Alsabbagh, 2024

Supervised Machine Learning for Text Analysis in R
Emil Hvitfeldt, 2022

AI/ ML Roadmap

TOP 10 Python Concepts for Job Interview

1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming

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Neural Networks and Deep Learning
Neural networks and deep learning are integral parts of artificial intelligence (AI) and machine learning (ML). Here's an overview:

1.Neural Networks: Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers: input layer, hidden layers, and output layer.

Each neuron receives input, processes it through an activation function, and passes the output to the next layer. Neurons in subsequent layers perform more complex computations based on previous layers' outputs.

Neural networks learn by adjusting weights and biases associated with connections between neurons through a process called training. This is typically done using optimization techniques like gradient descent and backpropagation.

2.Deep Learning : Deep learning is a subset of ML that uses neural networks with multiple layers (hence the term "deep"), allowing them to learn hierarchical representations of data.

These networks can automatically discover patterns, features, and representations in raw data, making them powerful for tasks like image recognition, natural language processing (NLP), speech recognition, and more.

Deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer models have demonstrated exceptional performance in various domains.

3.Applications Computer Vision: Object detection, image classification, facial recognition, etc., leveraging CNNs.

Natural Language Processing (NLP) Language translation, sentiment analysis, chatbots, etc., utilizing RNNs, LSTMs, and Transformers.
Speech Recognition: Speech-to-text systems using deep neural networks.

4.Challenges and Advancements: Training deep neural networks often requires large amounts of data and computational resources. Techniques like transfer learning, regularization, and optimization algorithms aim to address these challenges.

LAdvancements in hardware (GPUs, TPUs), algorithms (improved architectures like GANs - Generative Adversarial Networks), and techniques (attention mechanisms) have significantly contributed to the success of deep learning.

5. Frameworks and Libraries: There are various open-source libraries and frameworks (TensorFlow, PyTorch, Keras, etc.) that provide tools and APIs for building, training, and deploying neural networks and deep learning models.

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Complete Roadmap to learn Generative AI in 2 months 👇👇

Weeks 1-2: Foundations
1. Learn Basics of Python: If not familiar, grasp the fundamentals of Python, a widely used language in AI.
2. Understand Linear Algebra and Calculus: Brush up on basic linear algebra and calculus as they form the foundation of machine learning.

Weeks 3-4: Machine Learning Basics
1. Study Machine Learning Fundamentals: Understand concepts like supervised learning, unsupervised learning, and evaluation metrics.
2. Get Familiar with TensorFlow or PyTorch: Choose one deep learning framework and learn its basics.

Weeks 5-6: Deep Learning
1. Neural Networks: Dive into neural networks, understanding architectures, activation functions, and training processes.
2. CNNs and RNNs: Learn Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.

Weeks 7-8: Generative Models
1. Understand Generative Models: Study the theory behind generative models, focusing on GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).
2. Hands-On Projects: Implement small generative projects to solidify your understanding. Experimenting with generative models will give you a deeper understanding of how they work. You can use platforms such as Google's Colab or Kaggle to experiment with different types of generative models.

Additional Tips:
- Read Research Papers: Explore seminal papers on GANs and VAEs to gain a deeper insight into their workings.
- Community Engagement: Join AI communities on platforms like Reddit or Stack Overflow to ask questions and learn from others.

Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.

2 months are good as a starting point to get grasp the basics of Generative AI but mastering it is very difficult as AI keeps evolving every day.

Best Resources to learn Generative AI 👇👇

Learn Python for Free

Prompt Engineering Course

Prompt Engineering Guide

Data Science Course

Google Cloud Generative AI Path

Unlock the power of Generative AI Models

Machine Learning with Python Free Course

Deep Learning Nanodegree Program with Real-world Projects

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ENJOY LEARNING👍👍

Top Python libraries for generative AI

Generative AI is a branch of artificial intelligence that focuses on the creation of new content, such as text, images, music, and code. This is done by training models on large datasets of existing content, which the model then uses to generate new content.
Python is a popular programming language for generative AI, as it has a wide range of libraries and frameworks available.

Programming Practice Python

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To automate your daily tasks using ChatGPT, you can follow these steps:

1. Identify Repetitive Tasks: Make a list of tasks that you perform regularly and that can potentially be automated.

2. Create ChatGPT Scripts: Use ChatGPT to create scripts or workflows for automating these tasks. You can use the API to interact with ChatGPT programmatically.

3. Integrate with Other Tools: Integrate ChatGPT with other tools and services that you use to streamline your workflow. For example, you can connect ChatGPT with task management tools, calendar apps, or communication platforms.

4. Set up Triggers: Set up triggers that will initiate the automated tasks based on certain conditions or events. This could be a specific time of day, a keyword in a message, or any other criteria you define.

5. Test and Iterate: Test your automated workflows to ensure they work as expected. Make adjustments as needed to improve efficiency and accuracy.

6. Monitor Performance: Keep an eye on how well your automated tasks are performing and make adjustments as necessary to optimize their efficiency.

Several future trends in artificial intelligence (AI) are expected to significantly impact the current job market. Here are some key trends to consider:

1. AI Automation and Robotics: AI-driven automation and robotics are likely to replace certain repetitive and routine tasks across various industries. This can lead to a shift in the types of jobs available and the skills required for the workforce.

2. Augmented Intelligence: Rather than fully replacing human workers, AI is expected to augment human capabilities in many roles, leading to the creation of new types of jobs that require a combination of human and AI skills.

3. AI in Healthcare: The healthcare industry is likely to see significant changes due to AI, with the potential for improved diagnostics, personalized treatment plans, and more efficient healthcare delivery. This could create new opportunities for healthcare professionals with AI expertise.

4. AI in Customer Service: AI-powered chatbots and virtual assistants are already transforming customer service, and this trend is expected to continue. Jobs in customer service may evolve to focus more on complex problem-solving and emotional intelligence, as routine tasks are automated.

5. Data Science and AI: The demand for data scientists, machine learning engineers, and AI specialists is expected to grow as organizations seek to leverage AI for data analysis, predictive modeling, and decision-making.

6. AI Ethics and Governance: As AI becomes more pervasive, there will be an increased need for professionals specializing in AI ethics, governance, and regulation to ensure responsible and ethical use of AI technologies.

7. Reskilling and Upskilling: With the evolving nature of jobs due to AI, there will be a growing need for reskilling and upskilling programs to help workers adapt to new technologies and roles.

8. Cybersecurity and AI: As AI systems become more integrated into critical infrastructure and business operations, there will be a growing demand for cybersecurity professionals with expertise in AI-based threat detection and defense.

Overall, the rise of AI is expected to bring both challenges and opportunities to the job market, requiring individuals and organizations to adapt to the changing landscape of work and skills.

AI is the next biggest skill to learn.

AI experts are earing up to $200000+ per year.

Here are 4 FREE courses from Google and Microsoft that most people don't know:

https://microsoft.github.io/AI-For-Beginners/?

https://www.cloudskillsboost.google/paths/118

https://www.deeplearning.ai/courses/ai-for-everyone/

https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/

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