video1.0<iframe src="https://www.loom.com/embed/b702171d9f1549f1941c0f44088769c6" frameborder="0" width="1680" height="1260" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12601680Loomhttps://www.loom.com12601680https://cdn.loom.com/sessions/thumbnails/b702171d9f1549f1941c0f44088769c6-bc28d294abb2bc1c.gif285.633In this notebook, I introduce you to TensorFlow tensors and their crucial role in powering deep learning models. We explore the different types of tensors, including scalars, vectors, and matrices, and how to create them using functions like tf.constant and tf.random.normal. I also cover important tensor attributes, indexing, slicing, and matrix multiplication, which are essential for building neural networks. Additionally, we discuss activation functions and reduction operations that are commonly used in deep learning. I encourage you to engage with the practical exercises on tensor manipulation and building a simple neural network.