when considering such batch tensorthe first axis (axis is called the batch axis or batch dimension this is term you'll frequently encounter when using keras and other deep-learning libraries real-world examples of data tensors let' make data tensors more concrete with few examples similar to what you'll encounter later the data you'll manipulate will almost always fall into one of the following categoriesvector data-- tensors of shape (samplesfeaturestimeseries data or sequence data-- tensors of shape (samplestimestepsfeaturesimages-- tensors of shape (samplesheightwidthchannelsor (sampleschannelsheightwidthvideo-- tensors of shape (samplesframesheightwidthchannelsor (samplesframeschannelsheightwidthvector data this is the most common case in such dataseteach single data point can be encoded as vectorand thus batch of data will be encoded as tensor (that isan array of vectors)where the first axis is the samples axis and the second axis is the features axis let' take look at two examplesan actuarial dataset of peoplewhere we consider each person' agezip codeand income each person can be characterized as vector of valuesand thus an entire dataset of , people can be stored in tensor of shape ( dataset of text documentswhere we represent each document by the counts of how many times each word appears in it (out of dictionary of , common wordseach document can be encoded as vector of , values (one count per word in the dictionary)and thus an entire dataset of documents can be stored in tensor of shape ( timeseries data or sequence data whenever time matters in your data (or the notion of sequence order)it makes sense to store it in tensor with an explicit time axis each sample can be encoded as sequence of vectors ( tensor)and thus batch of data will be encoded as tensor (see figure features samples timesteps figure licensed to timeseries dat ensor