1596247200

# My First Work With PyTorch

Facebook launched PyTorch 1.0 early this year with integrations for Google Cloud, AWS, and Azure Machine Learning. In this example, I assume that you’re already familiar with Scikit-learn, Pandas, NumPy, and SciPy. These packages are important prerequisites for this tutorial.

## What is PyTorch?

It’s a Python-based scientific computing package targeted at two sets of audiences:

``````A replacement for NumPy to use the power of GPUs
a deep learning research platform that provides maximum flexibility and speed
``````

First, we need to cover a few basic concepts that may throw you off-balance if you don’t grasp them well enough before going full-force on modeling.

In Deep Learning, we see tensors everywhere. Well, Google’s framework is called TensorFlow for a reason! What is a tensor, anyway?

## Tensor

In Numpy, you may have an array that has three dimensions, right? That is, technically speaking, a tensor.

A scalar (a single number) has zero dimensions, a vector has one dimension, a matrix has two dimensions and a tensor has three or more dimensions. That’s it!

But, to keep things simple, it is commonplace to call vectors and matrices tensors as well — so, from now on, everything is either a scalar or a tensor.

## Imports and Dataset

For this simple example we’ll use only a couple of libraries:

• Scikit-learn: for train-test split
• Matplotlib: for data visualization
• PyTorch: for model training Here are the imports if you just want to copy/paste:
``````import torch
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
``````

As for the dataset, the Beer dataset, it can be found on this URL. https://www.kaggle.com/jtrofe/beer-recipes

This is a dataset of 75,000 homebrewed beers with over 176 different styles. Beer records are user-reported and are classified according to one of the 176 different styles. These recipes go into as much or as little detail as the user provided, but there’s are least 5 useful columns where data was entered for each: Original Gravity, Final Gravity, ABV, IBU, and Color.

We’ll use the linux terminal:

## Remove directorys and files

! rm -r input/ ! mkdir input/ ! cd input/

! ls

## Unzip Dataset

! unzip beer-recipes.zip

## Move zip file

!mv beer-recipes.zip input/beer.zip

## Move csv file

!mv recipeData.csv input/recipeDate.csv !mv styleData.csv

! ls input/

## Post- ETL

We are going to use a clean dataset.

#pytorch #beer #deep-learning #python #neural-networks #deep learning

1614329473

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For more details: https://contactforhelp.com/blog/how-to-fix-the-g-suite-email-not-working-issue/

#g suite email not working #g suite email not working on iphone #g suite email not working on android #suite email not working on windows 10 #g suite email not working on mac #g suite email not syncing

1607930471

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1599921480

## API-First, Mobile-First, Design-First... How Do I Know Where to Start?

##### Dear Frustrated,

I understand your frustration and I have some good news and bad news.

##### Bad News First (First joke!)
• Stick around another 5-10 years and there will be plenty more firsts to add to your collection!
• Definitions of these Firsts can vary from expert to expert.
• You cannot just pick a single first and run with it. No first is an island. You will probably end up using a lot of these…

### Good News

While there are a lot of different “first” methodologies out there, some are very similar and have just matured just as our technology stack has.

Here is the first stack I recommend looking at when you are starting a new project:

#### 1. Design-First (Big Picture)

Know the high-level, big-picture view of what you are building. Define the problem you are solving and the requirements to solve it. Are you going to need a Mobile app? Website? Something else?

Have the foresight to realize that whatever you think you will need, it will change in the future. I am not saying design for every possible outcome but use wisdom and listen to your experts.

#### 2. API First

API First means you think of APIs as being in the center of your little universe. APIs run the world and they are the core to every (well, almost every) technical product you put on a user’s phone, computer, watch, tv, etc. If you break this first, you will find yourself in a world of hurt.

Part of this `First` is the knowledge that you better focus on your API first, before you start looking at your web page, mobile app, etc. If you try to build your mobile app first and then go back and try to create an API that matches the particular needs of that one app, the above world of hurt applies.

Not only this but having a working API will make design/implementation of your mobile app or website MUCH easier!

Another important point to remember. There will most likely be another client that needs what this API is handing out so take that into consideration as well.

#### 3. API Design First and Code-First

I’ve grouped these next two together. Now I know I am going to take a lot of flak for this but hear me out.

##### Code-First

I agree that you should always design your API first and not just dig into building it, However, code is a legitimate design tool, in the right hands. Not everyone wants to use some WYSIWYG tool that may or may not take add eons to your learning curve and timetable. Good Architects (and I mean GOOD!) can design out an API in a fraction of the time it takes to use some API design tools. I am NOT saying everyone should do this but don’t rule out Code-First because it has the word “Code” in it.

You have to know where to stop though.

Designing your API with code means you are doing design-only. You still have to work with the technical and non-technical members of your team to ensure that your API solves your business problem and is the best solution. If you can’t translate your code-design into some visual format that everyone can see and understand, DON’T use code.

#devops #integration #code first #design first #api first #api

1600632000

## What is Pytorch ?

Pytorch is a Deep Learning Library Devoloped by Facebook. it can be used for various purposes such as Natural Language Processing , Computer Vision, etc

## Prerequisites

Python, Numpy, Pandas and Matplotlib

## Tensor Basics

What is a tensor ?

A Tensor is a n-dimensional array of elements. In pytorch, everything is a defined as a tensor.

#pytorch #pytorch-tutorial #pytorch-course #deep-learning-course #deep-learning

1596247200

## My First Work With PyTorch

Facebook launched PyTorch 1.0 early this year with integrations for Google Cloud, AWS, and Azure Machine Learning. In this example, I assume that you’re already familiar with Scikit-learn, Pandas, NumPy, and SciPy. These packages are important prerequisites for this tutorial.

## What is PyTorch?

It’s a Python-based scientific computing package targeted at two sets of audiences:

``````A replacement for NumPy to use the power of GPUs
a deep learning research platform that provides maximum flexibility and speed
``````

First, we need to cover a few basic concepts that may throw you off-balance if you don’t grasp them well enough before going full-force on modeling.

In Deep Learning, we see tensors everywhere. Well, Google’s framework is called TensorFlow for a reason! What is a tensor, anyway?

## Tensor

In Numpy, you may have an array that has three dimensions, right? That is, technically speaking, a tensor.

A scalar (a single number) has zero dimensions, a vector has one dimension, a matrix has two dimensions and a tensor has three or more dimensions. That’s it!

But, to keep things simple, it is commonplace to call vectors and matrices tensors as well — so, from now on, everything is either a scalar or a tensor.

## Imports and Dataset

For this simple example we’ll use only a couple of libraries:

• Scikit-learn: for train-test split
• Matplotlib: for data visualization
• PyTorch: for model training Here are the imports if you just want to copy/paste:
``````import torch
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
``````

As for the dataset, the Beer dataset, it can be found on this URL. https://www.kaggle.com/jtrofe/beer-recipes

This is a dataset of 75,000 homebrewed beers with over 176 different styles. Beer records are user-reported and are classified according to one of the 176 different styles. These recipes go into as much or as little detail as the user provided, but there’s are least 5 useful columns where data was entered for each: Original Gravity, Final Gravity, ABV, IBU, and Color.

We’ll use the linux terminal:

## Remove directorys and files

! rm -r input/ ! mkdir input/ ! cd input/

! ls

## Unzip Dataset

! unzip beer-recipes.zip

## Move zip file

!mv beer-recipes.zip input/beer.zip

## Move csv file

!mv recipeData.csv input/recipeDate.csv !mv styleData.csv

! ls input/

## Post- ETL

We are going to use a clean dataset.

#pytorch #beer #deep-learning #python #neural-networks #deep learning