Recurrent Neural Networks, or RNNs, are a type of artificial neural network designed for pattern recognition in sequential data. Unlike feedforward neural networks which assume that all inputs and outputs are independent of each other, RNNs capitalize on the inherent continuity in the data by maintaining a form of memory. These networks keep track of dependent features across time steps, making them particularly apt for processing data which involves time series or sequences like spoken language or stock prices.

• Preservation of Context: RNNs can remember information from previous time steps, this makes them more efficient at understanding the context in sequential data.
• Backpropagation through Time: Learning in an RNN happens through 'backpropagation through time', where the errors from the output are propagated back through the system to adjust the weights of the neural network.
• Internal Memory: Each neuron or unit in an RNN possesses an internal memory, which it uses to remember the information from previous time steps.
• Gradient Vanishing and Exploding Problem: While training an RNN, the gradient value used to update the weights can either become very small (vanish) or extremely large (explode). This can lead to the RNN not learning or learning too much respectively, hence these issues come under the constraints of RNNs.
• Versatility: RNNs find application in a variety of tasks, including text generation, sentiment analysis, language translation, and speech recognition.

Implementing RNNs requires cautious planning and understanding. You have to identify the problem accurately and whether it's a sequential task or not. Then you can move forward by selecting the architecture, initializing the parameters, and then training the network using an optimization algorithm. Hyperparameter tuning is particularly important in RNNs to prevent overfitting and to stabilize learning. It is critical to monitor the performance of the model periodically and make the necessary adjustments. Proper implementation of RNNs can unravel patterns unseen before and give you an edge in problem-solving.

• Intricate Pattern Detection: RNNs can detect intricate patterns in sequential data due to their ability to remember past inputs. This nature of 'long-term dependencies' allows for more complex tasks to be performed.
• Applicability: RNNs are applicable in a wide variety of areas such as natural language processing, text prediction, and machine translation which requires understanding the context in sequential data.
• Iterative Learning: The iterative learning approach of RNNs facilitates the improvement of model performance over time as the network can learn from its previous outputs.
• Flexibility: RNNs can process inputs of varying lengths, giving them the flexibility to handle different types of sequential data.

• Complex Training: The training of RNNs can be complex due to the 'vanishing gradient' problem which makes learning long-term dependencies challenging.
• Computationally Intensive: Due to their iterative nature, RNNs can be more computationally intensive to train than other types of neural networks.
• Lack of Parallelism: Training RNNs is an inherently sequential task which makes it hard to gain computational speed through parallel operations.
• Risk of Overfitting: RNNs can easily overfit on a training set, especially if it's not very large.

In conclusion, while RNNs come with certain constraints, their ability to detect intricate patterns makes them powerful tools in the field of machine learning. By leveraging their strengths and mitigating their disadvantages, RNNs can be of great value in various applications.