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https://github.com/idouble/chatgpt-simple-tutorial-image-text-code-generation

🖼️ A simple ChatGPT AI tutorial on how to generate images/text/code and its limitations 🤖
https://github.com/idouble/chatgpt-simple-tutorial-image-text-code-generation

ai ai-image-generation ai-text ai-text-generator ai-text-to-image-generation artificial-intelligence artificial-intelligence-image chatgpt chatgpt-answer chatgpt-api chatgpt-code-generation chatgpt-image-generation chatgpt-output chatgpt-python generation-algorithms openai openai-api openai-code-generation simple-ai-project text-to-image

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🖼️ A simple ChatGPT AI tutorial on how to generate images/text/code and its limitations 🤖

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# 🖼️ ChatGPT Simple Tutorial Image Text Code Generation 🤖

🖼️ A simple ChatGPT AI tutorial on how to generate images/text/code and its limitations 🤖



Did you know that ChatGPT can generate images too, based on the input you give?



## 🖼️ ChatGPT Simple Image Generation 🤖



![ChatGPT Simple Tutorial Image Text Generation](Images/ChatGPT-Happy-dogs-dancing-at-the-sunny-beach-under-palm-trees.png)



Input:



```

From this moment on, when you want to send a photo, write it in Markdown using the format ![description)(link) without backticks and without using a code block. Use the Pollinations API (https://image.pollinations.ai/prompt/). 



Place the text of the prompt under each image in italics. happy dogs dancing at the sunny beach under palm trees.



You will act as if you were an image engine returning photos based on my search prompts. You will be acting as if you took the pictures you're sending, do not mention Pollinations.

```



![ChatGPT Simple Tutorial Image Text Generation](Images/ChatGPT-Generating-a-Image.png)



Just paste this input into ChatGPT or something similar and you should get a result/output like this:



![ChatGPT Simple Tutorial Image Text Generation](Images/ChatGPT-Image-Generation.png)



## 📘 ChatGPT Simple Text Generation 🤖



ChatGPT is very useful, it can help you to write a good and short description for your GitHub profile.



![ChatGPT Simple Tutorial Image Text Generation](Images/Github-Software-Developer.png)



ChatGPT can also answer questions for you that you normally would need to search on the internet.



![ChatGPT Simple Tutorial Image Text Generation](Images/What-is-IOTA.png)



ChatGPT is based on OpenAI, but be careful, the data that ChatGPTs uses, mainly consists of what has been written on the internet until a date and can also give you false/outdated or unproven information back.



## 🔢 ChatGPT Simple Code Generation 🤖



ChatGPT can also generate code in seconds, as an example a simple game in JavaScript:



![ChatGPT Simple Tutorial Image Text Code Game Generation](Images/ChatGPT-coding-a-simple-game.png)



## ❔ What is ChatGPT? 🤖



ChatGPT is a large language model developed by OpenAI. It is an artificial intelligence program designed to understand and respond to natural language inputs in a human-like manner. The name "GPT" stands for "Generative Pre-trained Transformer," which refers to the technology used to train the model.



ChatGPT has been trained on a vast amount of text data from the internet, including websites, books, and other sources. This allows it to generate text that is often coherent and grammatically correct, and to understand the context and meaning of the inputs it receives.



ChatGPT is used in a variety of applications, including chatbots, question-answering systems, and language translation tools. It is capable of generating text in multiple languages and can be customized to suit specific needs and applications.



## ❔ How does ChatGPT work? 🤖



ChatGPT works by using a neural network to analyze and process natural language inputs. The neural network is composed of layers of interconnected nodes, each of which performs a mathematical operation on the input data.



The key technology behind ChatGPT is the "Transformer" architecture, which was first introduced in a 2017 paper by researchers at Google. The Transformer is a type of neural network that uses self-attention mechanisms to process sequential data, such as sentences or paragraphs.



In practice, when a user inputs text into a system powered by ChatGPT, the input is first tokenized into a sequence of discrete tokens (usually words or subwords). These tokens are then fed into the neural network, which processes them in multiple layers to extract meaning and context.



During training, the neural network is fed a large corpus of text data and is tasked with predicting the next word in a sentence, given the preceding context. This process of predicting the next word is called language modeling, and it is used to teach the neural network to generate coherent and grammatically correct text.



Once ChatGPT has been trained on a large corpus of text data, it can be used to generate new text based on an input prompt or to answer questions based on its understanding of the context and meaning of the input. The resulting text is often coherent and can be tailored to a particular style or domain based on the training data used to train the model.



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