Build a Convolutional Neural Network from Scratch using Numpy

Build a Convolutional Neural Network from Scratch using Numpy AI News, AI, AI tools, Innovation, itinai.com, LLM, Riccardo Andreoni, t.me/itinai, Towards Data Science - Medium 🔹🔹🔹 Exciting News for Middle Managers! Learn how to Build a Convolutional Neural Network from Scratch using Numpy! 🔹🔹🔹 Computer Vision is an essential tool in today's world, and understanding it is crucial for Data Science practitioners. In this article, we will guide you through the process of building a Convolutional Neural Network (CNN) using only the Python library Numpy. CNNs are specifically designed for image-related tasks like image classification, object localization, and image segmentation. They function similarly to the human visual cortex, making them highly effective for analyzing images. The traditional fully connected networks struggle with large images due to the massive number of parameters they require. CNNs solve this problem by implementing partially connected layers and weight sharing. A CNN comprises two main components: convolutional layers and pooling layers. Convolutional Layers: ➡️ Convolutional layers apply filters to the input image, highlighting different features like vertical or horizontal edges. These filters learn the values during training. Pooling Layers: ➡️ Pooling layers downsize the input image, reducing computational load and memory usage. They aggregate sections of the image into a single value, typically using a max pooling kernel. We have provided Python code examples that demonstrate the implementation of the convolutional and pooling layers using Numpy. You can find these examples in our GitHub repository. By experimenting with the code, you can gain a practical understanding of CNNs. Although our implementation may not achieve state-of-the-art performance, it still reaches 96% accuracy after a few epochs. If you're interested in expanding your knowledge of CNNs and computer vision, we recommend checking out the listed resources at the end of the article. They will provide valuable insights and further learning opportunities. ⭐️⭐️⭐️ Ready to elevate your company with AI? We have the right solutions for you! ⭐️⭐️⭐️ To stay competitive and evolve your company, it's important to identify automation opportunities, define KPIs, select the right AI solution, and implement gradually. At itinai.com, we offer AI solutions like the AI Sales Bot that automate customer engagement and manage interactions throughout the customer journey. 🌟 Visit our website itinai.com to learn more about how AI can redefine your sales processes and customer engagement. For advice on AI KPI management, reach out to us at hello@itinai.com. Stay updated on the latest AI insights by following us on Telegram at t.me/itinainews or Twitter @itinaicom. 🌐 Useful Links: 🔗 AI Lab in Telegram @aiscrumbot – free consultation 🔗 Build a Convolutional Neural Network from Scratch using Numpy - Towards Data Science – Medium 🔗 Twitter – @itinaicom Let's embrace the power of AI together for your company's success! 💪🚀