Hello World of ANN, RNN, and CNN
Padam Tripathi (Learner)
AI Architect | Generative AI, LLM | NLP | Image Processing | Cloud Architect | Data Engineering (Hands-On).
Artificial Neural Network (ANN)
Overview: ANNs are the foundational neural network models composed of interconnected layers of nodes (neurons). They include an input layer, one or more hidden layers, and an output layer. Each connection has an associated weight, which is adjusted during training to minimize prediction errors.
Use Cases: ANNs are versatile and can be used for various tasks, including:
When to Use: Use ANNs for general-purpose machine learning tasks where the data does not have a specific structure, such as tabular data.
ANN Diagram:
Recurrent Neural Network (RNN)
Overview: RNNs are specialized neural networks for sequential data. They have connections that form directed cycles, allowing them to maintain a memory of previous inputs. This makes RNNs suitable for tasks where the order of inputs matters.
Recurrent Neural Networks (RNNs) are designed to handle sequential data, making them particularly well-suited for Natural Language Processing (NLP) tasks. They have an internal state that allows them to maintain information about previous inputs, capturing dependencies and context over sequences of words or characters.
RNN has Feedback Loop because of this it has Short Term Memory.
Use Cases: RNNs are ideal for:
When to Use: Use RNNs when dealing with sequential data where context and order are important.
RNN Images:
Convolutional Neural Network (CNN)
Overview: CNNs are designed for processing grid-like data, such as images. They use convolutional layers to automatically and adaptively learn spatial hierarchies of features, followed by pooling layers to reduce dimensionality and fully connected layers for classification.
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Use Cases: CNNs excel at:
When to Use: Use CNNs for tasks involving image data or other grid-like data structures.
CNN Image:
Read Colah's blog for more details - ANN, RNN & CNN
ANN Implementation in Jyputer Notebook:
CNN Implementation in Jyputer Notebook:
RNN Implementation in Jyputer Notebook:
Summary Table
By choosing the appropriate neural network architecture (ANN, RNN, or CNN), you can leverage their strengths to effectively address specific types of machine learning problems.