Chic perennial Intelligence

  • A 4-step guide to making a deployment-ready deep learning model 

    A 4-step guide to making a deployment-ready deep learning model 

    The model has been trained and dumped, what next? Deploying a deep learning model in production is a complex task that requires careful attention to both technical and practical details. You want to ensure that your model performs well and delivers accurate results, but you also need to consider other factors such as user experience,…

  • Swish Activation function

    Swish Activation function

    The smooth and non-monotonic function that can be used in place of the commonly used ReLU activation function. Introduction to Activation Functions In machine learning and deep learning, an activation function is a mathematical function that is applied to the output of a neural network layer to introduce non-linearity into the network, allowing it to…

  • Squeeze-and-Excitation: Enhancing CNNs for Improved Feature Representation

    Squeeze-and-Excitation: Enhancing CNNs for Improved Feature Representation

    An Attention Mechanism for Channel-Wise Feature Enhancement Introduction Squeeze-and-Excitation (SE) Networks are a type of artificial neural network that helps computers better understand and recognize images. They do this by focusing on the important parts of an image and ignoring the unimportant parts. The SE module in the network is made up of two main…

  • Mastering the Fundamentals: A Quick Look at Quantum Gates

    Mastering the Fundamentals: A Quick Look at Quantum Gates

    Unlocking the mysteries of quantum computing with the power of gates. Introduction A quantum gate is a basic building block of quantum computation, which performs a specific unitary operation on a quantum state. A unitary operation is a mathematical operation that preserves the overall probability of the state, and quantum gates are used to manipulate…

  • Unleashing the Full Potential of Deep Learning Models: A Guide to Quantization Techniques

    Unleashing the Full Potential of Deep Learning Models: A Guide to Quantization Techniques

    Photo by Lucas Pezeta Precision at a fraction of the size: Experience the power of quantization for your deep learning models Introduction Model quantization is a technique for reducing the precision of the weights and activations of a neural network model. This process can be used to decrease the model’s memory footprint and computational complexity,…