This is the type of data contained in the tensor, 3D tensors of shape (samples, timesteps,features), 4D tensors of shape (samples, height, width, channels), 5D tensors of shape (samples, frames, height, width, channels). TensorFire is a framework for running neural networks in the browser, accelerated by WebGL. n tells us the number of indexes required to access a specific element within the structure. The relationship between neural networks and the tensor theory provides theoretical support for DNNs on one hand (Cohen et al., 2016), and enables the theory to be effectively applied in practical problems on the other hand (Cohen & Shashua, 2016). I'll see you in the next one! Thus every minute is encoded as a 3D vector, an entire day of trading is encoded as a 2D tensor of shape (390, 3) (there are 390 minutes in a trading day), and 250 days’ worth of data can be stored in a 3D tensor of shape (250,390, 3). Every minute, we store the current price of the stock, the highest price in the past minute, and the lowest price in the past minute. Structure can be explicit as represented by a graph or implicit as induced by adversarial perturbation. Because each frame can be stored in a 3D tensor (height, width, color_depth), a sequence of frames can be stored in a 4D tensor (frames, height, width, color_depth), and thus a batch of different videos can be stored in a 5D tensor of shape (samples, frames, height, width, color_depth). The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. So, we can redefine the two pairs as  and