Skip to main content

2. Our environment in Anaconda

2. Our environment in Anaconda


We will use this free and open source, with applications related to: large-scale data processing, predictive analytics, scientific computing; that aims to simplify package management and deployment. Ref

2.1. How to install Anaconda


Easy! Follow my steps:
2. Slide to the bottom of the page
3. Click on “Download Anaconda”
4. A new tab will open (see below), where you decide if you want Python 2.7 or 3.6



I recommend the version 3.6
5. Click on the chosen version and follow the downloading process (read the conditions, accept them, select where to store the Navigator…)
6. I recommend installing it inside the User Directory (in case you are sharing the computer with other Users), for instance in the Desktop

All set!


2.2. How to create our environment


If installing Anaconda was easy, this is a piece of cake! Follow my steps:
1. Open Anaconda Navigator (I’d keep it in Dock so that it’s easier to access)


2. On the left menu, select Environments
3. You’ll find the default environment base(root) as it appears on the image.




In the bottom menu, we can Create a New Environment, Clone, Import, or Remove the existing environments.

If we click on the green arrow next to the Name of the Environment, we can Open it in the Terminal, with Python, with iPython and with Jupyter Notebook.

On the right hand part, we have the packages that Anaconda Navigator offers, with a small description and the version.

Trick: if the version appears in blue, it means that there exists a higher version of such package; i.e. it is updateable!



2.3. Why things may not work sometimes


Well, in life many things do not work and there is no explanation found yet. However, it is your day of luck, and the reason for this  is probably that you don’t have the package downloaded. Then go to Anaconda Navigator > Environments > “MY CREATED ENVIRONMENT” and in the top menu, there is a sliding bar containing: Installed, Uninstalled, Updatable, Selected, All. There you go, if something does not work (you can look at it in the searching bar at the right), it is either because you have not downloaded it, or because you need to update it.

Trick: another option is from the Terminal (if Mac) or Command Window (if Window), install or update any package. Example:
pip install numpy

Another trick: install things from Anaconda (in your Terminal/Command Window)
conda install numpy
conda update scikitlearn
This will install numpy (if inexistent) and update the ScikitLearn current version.

Comments

Post a Comment

Popular posts from this blog

4. Understand how a Neural Network works

4. Understand how a Neural Network works 4.1. Graphical Understanding In order to start in this world, it is important first to have a visual perception of what we are going to deal with, and understand the basics of how Neural Networks work, in the raw form. Image Source A Neural Network (NN) is nothing else than a net of perceptrons that are linked so that input fires another network that produces an output.  Of course, a NN has assigned values that allow to make further calculations and learning.   Image source So far the two images above represent a simple NN with 1 input, 1 output and 1 hidden layer. The hidden layer is said to be dense (each neuron in a layer x is connected to all neurons in the layer x-1 and all the neurons in the layer x+1). Depending on how the neurons are organized and how the connections are made, we can find many different types of NN:   Types of NN (image source and explanation) 4.2. Simple Neural
Post a Comment
Read more

Glossary of terms

  Image Source Machine learning (ML) : Subfield of Artificial Intelligence (AI) in the field of computer science focused on the development of intelligent systems, capable of "learning" (i.e., progressively improve performance on a specific task) with data , without being explicitly programmed. Ref Chronology : Build with GoogleCloud - Chronology tree for ML Perceptron : algorithm for supervised learning of binary classifiers (functions that can decide whether an input, represented by a vector of numbers, belongs to some specific class or not → classification in two parts). “It is a single layer NN”, thus in order to learn how a NN works, we should learn how a perceptron works. Weights : show the strength of the particular node Bias value : allows to shift the activation function Activation function : map input between required values like (0, 1) or (-1, 1).      Fig : Perceptron Deep learning : (also known as deep stru
Post a Comment
Read more

1. Definition

  1. Definition 1.1. What is Deep Learning?   “Deep learning is the state-of-the-art approach across many domains, including object recognition and identification, text understating and translation, question answering, and more. In addition, it is expected to play a key role in many new usages deemed almost impossible before, such as fully autonomous driving”, Ronny Ronen It looks like a lot of fun, right? I am sure you already know what you need Deep Learning for, which I bet is something fascinating. So without making it longer… Let’s start diving!! I will try to make this manual as concise and self-explanatory as possible. I still lack of experience so I apologize in advance if there is anything that is not clear or does not work. If you let me know I’ll do my best to improve the explanation or fix the problems as soon as I can. By the way, in this handeable manual we can find a glossary of terms in the end. I will try to set in italics (or other marking?) everytime a word a
Post a Comment
Read more