Explore the different types of Neural Network architectures and how NNaaS integrate blockchain and cloud computing.
1. Convolutional Neural Networks (CNNs)
CNNs are designed to process grid-like data, such as images. They use convolutional layers to automatically and adaptively learn spatial hierarchies of features.
How It Works:
Convolutional Layers: Apply filters to detect features like edges, textures, and shapes.
Pooling Layers: Reduce the spatial size of the representation, making the network more efficient.
Fully Connected Layers: Combine features to make predictions (e.g., image classification)
Use Cases in NNaaS:
Image Recognition: Identifying objects in medical imaging (e.g., tumors in X-rays).
Pattern Detection: Analyzing visual data for trading patterns, climate heatmaps, sales graphs.
2. Recurrent Neural Networks (RNNs)
What is it:
RNNs are designed for sequential data, such as time series or text. They have a “memory” that captures information about previous inputs.
How It Works:
Recurrent Connections: Each node in the network takes input from the current time step and the previous time step.
Hidden State: Maintains a representation of the sequence up to the current point.
Use Case Visual:
RNN predicting Bitcoin price movements using historical volatility patterns
Use Cases in NNaaS:
Time-Series Prediction: Forecasting stock prices or patient health metrics.
Natural Language Processing (NLP): Analyzing sentiment in financial news, customers reviews.
3. Long Short-Term Memory Networks (LSTMs)
What is it:
LSTMs are a type of RNN designed to handle long-term dependencies in sequential data. They use “gates” to control the flow of information.
How It Works:
Input Gate: Decides what new information to store.
Forget Gate: Decides what information to discard.
Output Gate: Decides what information to output.
Healthcare Application:
LSTM model forecasting sepsis risk 24hrs early from ICU sensor data.
Use Cases in NNaaS:
Predictive Analytics: Forecasting patient outcomes based on historical data.
Trading: Predicting market trends over long periods.
4. Transformer Models
What is it:
Transformers are designed for sequence-to-sequence tasks, such as language translation or text generation. They use self-attention mechanisms to weigh the importance of different parts of the input.
How It Works:
Self-Attention: Computes relationships between all words in a sentence, regardless of their distance.
Positional Encoding: Adds information about the position of words in the sequence.
Financial Analysis:
Transformer assessing market sentiment from news snippets.
Use Cases in NNaaS:
Natural Language Processing (NLP): Generating reports or analyzing financial news.
Multimodal Models: Combining text and image data for advanced analytics.
Cloud & Blockchain Integration
🌨️ Cloud Infrastructure and Blockchain
NNaaS uses cloud computing to provide a secure and scalable infrastructure, powering the Train Your Own Model function without needing complex hardware.
Blockchain technology integration for decentralized data storage.
🖇️ APIs
We develop APIs to allow researchers to integrate NNaaS models into their workflows (e.g., linking a climate model to a weather monitoring system or linking their sale forecasting model to operational software of their business).
