[PT2021] What Is The Best Way To Get Started In A Deep Learning Career?

[IOS 20]

 Beginning the journey to your Deep Learning Career?

Deep learning can be a complex and intimidating field for novices. Concepts
like hidden layers, convolutional neural network, and backpropagation keep
popping up when you try to comprehend the deep learning concepts.

It's not easy - especially if you go on an unorganized learning route and
do not cover the basics first. You'll end up wandering around in a foreign
city like the tourist who has no map!

There's a good thing - you don't require advanced degrees or a Ph.D. to
study and master deep learning. But there are certain key concepts that you
must be aware of (and know about) before you plunge into deep learning.

I'll cover five of these essential concepts in this article. I also
recommend going through the resources below to enrich your learning
experience
A Brief Introduction Neural Networks (Free Course)- Computer Vision using
Deep Learning A Comprehensive Learning Path for Deep Learning in 2020

The five most important things to know before you begin your deep-learning
journey are:
- Getting your system ready- Python programming

- Linear Algebra and Calculus

- Probability and Statistics

- Key Machine Learning Concepts
1. Getting your System Ready for Deep Learning

In order to master the latest skill, for instance, cooking, you would first
require all the tools. For cooking, you will need equipment like a knife, a
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have to learn how to make use of the tools provided to you.
cooking_tools_deep_learning

In the same way, it is crucial to setup your system for deep-learning, have
some knowledge of the tools you would need and know how to utilize them.

This is a great guide to get started with Git and the basic Git command:
Git - Tutorial.

The Deep learning boom has not only led to path-breaking research in the
field of AI but has also broken new barriers in computer hardware.
GPU (Graphics Processing Unit):

You will require the GPU to process video and image data for most deep
learning projects. You can build an advanced model of deep learning on your
PC or laptop with no GPU, but this would be extremely time-consuming to do.
The primary benefits of a GPU has to offer are:

It allows parallel processing.

In a CPU+GPU combo the CPU assigns difficult tasks to GPU and performs
other tasks itself, thus saving much time.
Here's a great video explaining the difference between a CPU and GPU:

The best feature? You don't need to buy an GPU or have one installed on
your machine. There are a variety of Cloud Computing resources that provide
GPUs either for no cost or at a affordable cost. There are also a handful
of GPUs that come preinstalled with some practice data sets and tutorials
that are preloaded. A few of them include Paperspace Gradient, Google Colab
as well as Kaggle Kernels.

On the other side, there are fully-fledged servers as well which need some
installation and some modifications such as Amazon Web Services EC2.
Data Structures and their use in Python

Python offers a variety of data structures that we can use for different
purposes. Each data structure has distinct properties that we can utilize
to store different kinds of data and data types. These properties include:

The term "ordered" means that there is a certain sequence in which the
elements within the data structure are stored. However, no matter what
method or time we employ it, this order will remain the same (unless we
change it explicitly)

Immutable: This means the structure of data cannot be changed. If a
structure of data can be mutable it implies that it is able to be modified
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