video1.0<iframe src="https://www.loom.com/embed/dc65a5c9130c40caa3372e612d4d4761" frameborder="0" width="1680" height="1260" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12601680Loomhttps://www.loom.com12601680https://cdn.loom.com/sessions/thumbnails/dc65a5c9130c40caa3372e612d4d4761-8b2163a4aa6a4c98.gif441.731In this video, I introduce the foundational concepts of NumPy that are essential for understanding deep learning, focusing on arrays, shapes, and mathematical operations. We explore how to create NumPy arrays, perform matrix multiplication, and utilize broadcasting, which simplifies operations across different dimensions. I also discuss various activation functions like ReLU and sigmoid, and the importance of understanding tensor shapes for neural networks. I encourage you to engage with the material by practicing these concepts, as they are crucial for building effective deep learning models.