Technology

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).