a light weight framework to support asynchronous python

The posted video is a presentation to sanic framework for beginner level understanding.

a light weight framework to support asynchronous python

The posted video is a presentation to sanic framework for beginner level understanding.

categorical data

Some useful methods in R that helps while working with the similar type of data

a) factor

multiple values are considered as categories and neglicted duplicate values

eg;

survey_vector <- c(“M”, “F”, “F”, “M”, “M”)factor_survey_vector <- factor(survey_vector)

b) levels

placing label for the categories

levels(factor_survey_vector) <-c("Female","Male")factor_survey_vector

c) summary

similar to the way there exist summary method in pandas in python here we’ve summary method

`summary(my_var)`

that’s all for now. Keep learning.

cont.

Similar to numpy where exist multi-dimensional array, the pytorch tensorflow are build up of tensors.

ever heard of it. Let’s begin.

- They are same as numpy arrays except that they have flexibility to run in GPU.

Types of

- FloatTensor: 32-bit float
- DoubleTensor: 64-bit float
- HalfTensor: 16-bit float
- IntTensor: 32-bit int
- LongTensor: 64-bit int

some basic functions

- torch.add()
- torch.sub()
- torch.mm() <matrix multiplication>
- torch.div()
- torch.t()
- torch.cat()
- a.reshape()

# converting the numpy array to tensor

- tensor = torch.from_numpy(a)

In next section lets discuss more about the common pytorch Modules… part 3 .