1. Deep Learning:
- Definition: Deep learning is a subset of machine learning that uses neural networks with many layers (deep neural networks) to model complex relationships in data. It is inspired by the structure of the human brain, where neurons send signals to each other to process information.
- Neural Networks:
- Composed of nodes (or artificial neurons) organized into layers:
- Input Layer: Receives data.
- Hidden Layers: Process data through interconnected nodes.
- Output Layer: Produces the final prediction or classification.
- How Deep Learning Works:
- Forward Propagation: Data flows through the network from input to output.
- Weight Assignment: Each node assigns a weight to the input features.
- Training Process: The model compares its prediction to the actual output, calculates the error, and adjusts the weights to minimize this error. This process is repeated many times until the model performs accurately.
- Advantages of Deep Learning:
- Autonomous Feature Extraction: Unlike traditional machine learning, deep learning models can identify important features on their own without requiring human-labeled features.
- Suitability for Complex Tasks: Especially useful for tasks like image classification, object detection, and natural language processing (NLP), where the relationships in data are complex.
- Challenges:
- Data Requirements: To train a deep learning model, large datasets are necessary (e.g., millions of images) to achieve accurate results.
- Cost: The computational power required for deep learning is high, making it more expensive than traditional machine learning approaches.
2. Deep Learning vs. Traditional Machine Learning:
- Traditional Machine Learning:
- Best suited for structured data (e.g., databases, spreadsheets) and labeled data.
- Common tasks include classification, regression, and recommendation systems (e.g., predicting customer churn).
- Deep Learning:
- Better suited for unstructured data (e.g., images, videos, and text).
- Tasks like image classification and natural language processing (NLP), where the model needs to recognize patterns and relationships between unstructured data points.
- Example:
- A phone company can use machine learning to predict customer churn based on structured customer data.
- Deep learning, however, would be used to analyze social media mentions to understand sentiment or analyze video content.
3. Generative AI:
- Definition: Generative AI uses deep learning models (particularly transformer neural networks) to generate new content, such as text, images, and music. It involves processing large datasets to learn patterns and then generating responses or creative works based on that knowledge.
- Transformer Networks:
- Input (Prompt): The sequence you provide to the model (e.g., a text prompt).
- Processing: Unlike traditional models, transformers process the entire input sequence in parallel, speeding up training and enabling the use of much larger datasets.
- Output (Response): The model generates an output sequence based on the input, which could be a continuation of text, a translation, or even a generated image or video.
- Large Language Models (LLMs):
- Training on Massive Datasets: These models are trained on vast amounts of text data and contain billions of parameters, allowing them to capture a wide range of human knowledge.
- Applications:
- Language Translation: Translating text from one language to another.
- Text Generation: Writing articles, stories, poems, or even code.
- NLP Tasks: Summarizing long articles, answering questions, and generating conversational responses.
- Example:
- Using Amazon Bedrock or similar platforms, a user can interact with pre-trained models to generate text or build custom applications, such as generating a song or providing an explanation of complex concepts like large language models.
