HUGGING FACE
Hugging Face is a central hub for all things related to Natural Language Processing (NLP) and, increasingly, other areas of machine learning like computer vision and audio processing. It’s become an incredibly popular platform for developers, researchers, and enthusiasts in the AI field.
CPU VS GPU
CPU (Central Processing Unit)
- Role: The CPU is the “brain” of the computer. It handles the majority of the processing tasks, including running the operating system, executing applications, and managing input/output operations.
- Architecture: CPUs are designed for general-purpose computing. They have a few powerful cores that can execute instructions quickly and efficiently. They are optimized for sequential processing, meaning they handle tasks one after another in a linear fashion.
- Strengths:
- Excellent at handling complex logic and decision-making.
- Fast execution of individual instructions.
- Good at multitasking different types of tasks.
GPU (Graphics Processing Unit)
- Role: The GPU is specialized in handling graphics rendering and parallel processing. It’s designed to accelerate the creation of images, videos, and other visual content.
- Architecture: GPUs have a massively parallel architecture with thousands of smaller cores. They are optimized for performing the same operation on multiple pieces of data simultaneously.
- Strengths:
- Highly efficient at performing repetitive calculations on large datasets.
- Excellent at handling parallel tasks, such as rendering graphics, processing images, and running simulations.
- Crucial for tasks like gaming, video editing, 3D modeling, and machine learning.
Key Differences Summarized
| Feature | CPU | GPU |
|---|---|---|
| Primary Role | General-purpose processing | Graphics rendering and parallel processing |
| Architecture | Few powerful cores, optimized for serial processing | Many smaller cores, optimized for parallel processing |
| Strengths | Complex logic, fast instruction execution, multitasking | Repetitive calculations, parallel tasks, graphics rendering |
| Use Cases | Operating system, applications, general computing | Gaming, video editing, 3D modeling, machine learning |
Imagine a CPU as a skilled chef who can prepare a complex meal with many different dishes, handling each step with precision. A GPU is like an army of kitchen helpers who can efficiently chop vegetables, peel potatoes, and perform other repetitive tasks in parallel, allowing the chef to focus on the more complex aspects of the meal.
NEURAL NETWORKS
https://aws.amazon.com/what-is/neural-network
1. Neurons and Nodes:
- Biological Neurons: The human brain is made up of billions of interconnected neurons. Each neuron receives signals from other neurons through connections called synapses, processes those signals, and then transmits its own signal to other neurons.
- Artificial Nodes: Neural networks mimic this with artificial “neurons” called nodes. These nodes receive input, perform a simple computation, and pass the result to other nodes.
2. Connections and Weights:
- Synapses: In the brain, the strength of the connection between neurons (synapses) determines how much influence one neuron has on another. Learning and memory are associated with changes in the strength of these connections.
- Weights: Neural networks represent connection strength with numerical values called “weights.” During training, the network adjusts these weights to learn the desired mapping between inputs and outputs.
3. Layers and Networks:
- Neural Networks: Neurons in the brain are organized into complex networks with different layers and specialized regions.
- Artificial Neural Networks: Similarly, artificial neural networks are structured in layers. Input is received by the input layer, processed through one or more hidden layers, and the result is produced by the output layer.
4. Signal Transmission and Activation:
- Action Potentials: Neurons communicate by sending electrical signals called action potentials. Whether a neuron “fires” and sends a signal depends on the combined strength of the signals it receives.
- Activation Functions: In neural networks, each node has an “activation function” that determines whether it will pass its result to the next layer. This function introduces non-linearity, which is crucial for the network to learn complex patterns.
5. Learning and Adaptation:
- Synaptic Plasticity: The brain’s ability to change the strength of connections between neurons in response to experience is called synaptic plasticity. This is the biological basis of learning.
- Training Algorithms: Neural networks learn through training algorithms that adjust the weights of connections based on the difference between the network’s output and the desired output. This process is analogous to how the brain learns from experience.
Important Considerations:
- Simplified Model: It’s crucial to remember that artificial neural networks are a highly simplified model of the human brain. The brain is vastly more complex and nuanced.
- Different Mechanisms: While inspired by the brain, neural networks use different mechanisms for computation and learning. For example, they typically rely on backpropagation for training, which is not believed to be how the brain learns.
STUDY
VEO: https://blog.google/technology/google-labs/video-image-generation-update-december-2024
Generative AI: https://www.sequoiacap.com/article/generative-ai-a-creative-new-world/
Sam Altman Interview: https://www.bloomberg.com/features/2025-sam-altman-interview/
ADOBE CREATIVE TOOLS
https://www.youtube.com/@AdobeCreativeCloud
Google Deepmind
https://deepmind.google/
Google Gemini
An AI Tool for Exploration and Learning Gemini is an advanced artificial intelligence designed to assist users in exploring various topics and expanding their knowledge. As a large language model, Gemini can process information and respond to questions in a comprehensive and informative way. Users can ask Gemini about a wide range of subjects, from history and science to literature and current events. Additionally, Gemini can generate creative content, translate languages, and summarize complex texts. While still under development, Gemini offers a unique opportunity to interact with AI and discover new perspectives on the world around us.
https://aistudio.google.com/
Adobe Firefly
How to use FIREFLY
ChatGPT
https://openai.com/blog/chatgpt/
https://chat.openai.com
RUNWAY ML Tutorial
https://academy.runwayml.com/
MIDJOURNEY – Steps
Discord > https://discord.com/
Instructions: https://midjourney.gitbook.io/docs
DISCORD INSTRUCTIONS > https://support.discord.com/hc/en-us/articles/360033931551-Getting-Started
MIDJOURNEY > https://midjourney.com/
You can generate your prompts via ChatGPT > https://chat.openai.com/chat
Q: How do I upload or use an image from my hard-drive for an image prompt?
- Drag an image into Discord.
- Press enter to send it in the chat.
- Click the image in discord so that it’s fullscreen.
- Right click it and press “copy image address”
- Type /imagine and then paste the address in the prompt area.