Master AI with Ease - Free Guide Inside!
Check out my latest eBook on Prompt Engineering and how to work with AI. It’s free for a short time!
2025 Update!!!
This book is not free anymore (sorry!!!). But if you want to unlock the full potential of AI, you can get still get the free sample and the first chapter at futureproofskillshub.com/prompt-engineering.
The book“Practical Prompt Engineering” — features 250+ expert-level prompts and comprehensive guides for ChatGPT and other AI tools. It includes industry-specific templates for finance, healthcare, and manufacturing. Sample Available in paperback on Amazon, and as an ebook on Apple Books, Barnes & Noble, and Kobo.
Master the complete tech stack at futureproofskillshub.com/books — from AI to Python, SQL, and Linux fundamentals. Plus, discover how to maintain peak performance and work-life balance while advancing your technical career in “Discover The Unstoppable You”.
This is a special edition of my newsletter, and I'm excited to share news about my latest eBook, "Practical Prompt Engineering: A Step-by-Step Guide to Using AI Language Models". To celebrate that, I have a gift for everyone—a FREE copy of the eBook!
The book is now available on Amazon and it’s free for a limited time, so grab it while the promotion lasts!
You can find the eBook here: https://www.amazon.com/dp/B0D32179DJ
Below is a a short extract from the book. It will give you an idea of what this eBook covers and what you can gain from it. If you find the book helpful, please leave a review. It really helps me and helps others find the book too!
Intuitive Understanding of AI Concepts
Before we look deeper into Prompt Engineering, it's useful to first understand how it works. Here, we'll explain all AI concepts in simple terms to help you grasp each idea better. Let’s try to intuitively understand how everything works.
Standard Computer Programs as Traditional Chefs
Imagine a standard computer program as a traditional chef in a kitchen. You provide this chef with a recipe (rules/commands) and ingredients (data). The chef follows the recipe exactly to prepare a dish (output). The quality of the dish depends on both the quality of the ingredients and the precision of the recipe.
In this scenario, the process is straightforward and deterministic – the same recipe and ingredients will always produce the same dish.
TV shows "America's Test Kitchen" and "Julia Child's Cooking Shows" are just like standard computer programming. In these shows the hosts carefully follow recipes, focusing on precision and technique to achieve consistent results. These cooking shows focus on the importance of following recipes step by step.
This is just like standard computer programming, where certain instructions (recipes) are provided, and the computer (chef) follows them exactly to create a consistent output (dish).
Machine Learning Models as Apprentice Chefs
Now, think of a machine learning algorithm as an apprentice chef who is learning to create recipes. Instead of giving them a specific recipe, you provide them with a bunch of dishes (answers/output) along with their ingredients (data/input). The apprentice chef’s goal is to figure out the recipes (models/rules) that could have been used to make these dishes.
The apprentice chef (machine learning model) experiments with different combinations and cooking methods, trying to match the provided dishes. This process is similar to the training phase in machine learning, where the algorithm iteratively adapts its understanding (the model) to get as close as possible to the provided examples.
Once the apprentice chef has tried and learned enough to reliably recreate the dishes or even create new dishes of similar quality, they have effectively written their own cookbook (developed a model). This cookbook is a set of guidelines (rules) that the chef has obtained from their learning experience, which can now be used to cook new dishes (make predictions or decisions based on new data).
Just as the apprentice chef can learn new cuisines or adapt recipes based on new dietary restrictions or ingredients, a machine learning model can be updated with new data or retrained to refine its rules and adapt to new situations.
Machine Learning is more like "MasterChef" TV show, where contestants are given a set of ingredients (data) and must create a dish (model/output) that is judged. They don't have a specific recipe to follow; instead, they must use their culinary skills (algorithm) to create something unique. This is very similar to machine learning, where the model learns from existing data and outputs to create predictions or new data interpretations.
In another variant of this contest called “Replication Challenge”, contestants try to recreate a chef's signature dish. Contestants see the finished dish and must figure out how to replicate it, learning and deducing the cooking process (developing a model) from the final product (output/answers).
Neural Networks
Neural networks and deep learning models are composed of layers made up of neurons, which use weights, biases, and activation functions to transform input data into meaningful outputs. The learning process involves adjusting these weights and biases to minimize the difference between the model’s predictions and the actual data, using backpropagation and optimization algorithms.
This might sound too technical for some of us. Let’s try to understand these concepts in a simple way, through analogy of: asking friends for a restaurants recommendation:
Group of Friends as Layers
Think of the entire network of your friends, their friends, and so on, as layers in a neural network. Your immediate friends are the first layer (input layer), their friends are the next layer (hidden layer), and this can continue with friends of friends forming additional layers. Just as in a deep learning model, where each layer has a specific function and contributes to the overall processing, each group of friends plays a role in gathering and filtering information.
Knowing Each Other as Weights
The strength of the relationship between your friends (how well they know each other) can be seen as the 'weights'. In a neural network, weights adjust during training to optimize the network's performance. Similarly, the strength of connections between friends might change over time, influencing how they share and interpret information.
Individual Knowledge as Biases
The personal knowledge, preferences, and experiences of each friend act as 'biases'. In a neural network, biases help to adjust the output along with the weighted inputs. Each friend’s bias (like their preference for certain types of food) influences their part of the recommendation.
Asking for a Recommendation as Processing Input
When you ask your friends to recommend a good restaurant, it's like providing input to a neural network. Your friends process this request, consider their knowledge and preferences (biases), and consult their connections (weights).
Group Recommendation as Output
The final restaurant recommendation you receive is the output of this network. It's a result of processing through multiple layers of friends, each contributing with their weighted opinions and biases, much like how a deep learning model processes input data through its layers to arrive at an output.
Learning Over Time
Just as a neural network learns and improves its accuracy over time with more data and training, your network of friends might become more accurate at giving you better recommendations as they learn more about your preferences and as their own knowledge and connections (weights and biases) evolve.
I hope you will find that this analogy nicely encapsulates the concepts of layers, weights, biases, and the process of input-to-output transformation in deep learning models.