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<h2>Beginning the journey to your Deep Learning Career?</h2><p>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.</p><p>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!<br><br>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.<br><br>I'll cover
five of these essential concepts in this article. I also recommend
going through the resources below to enrich your learning experience</p><h2>A Brief Introduction Neural Networks (Free Course)</h2><h4>- Computer Vision using Deep Learning</h4><h2> A Comprehensive Learning Path for Deep Learning in 2020</h2><p>The five most important things to know before you begin your deep-learning journey are:</p><h2>- Getting your system ready</h2><h4>- Python programming</h4><p>- Linear Algebra and Calculus<br><br>- Probability and Statistics<br><br>- Key Machine Learning Concepts</p><h2>1. Getting your System Ready for Deep Learning</h2><p>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 cooking pan and, of course, a gas stove! Check out the brand
new web site <a href="https://pureinfohub.com/" rel="dofollow">https://pureinfohub.com/</a> There are many tools available, but you will also have to learn how to make use of the tools provided to you.</p><h2>cooking_tools_deep_learning</h2><p>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.<br><br>This is a great guide to get started with Git and the basic Git command: Git - Tutorial.<br><br>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.</p><h2>GPU (Graphics Processing Unit):</h2><p>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:</p><p>It allows parallel processing.</p><p>In a CPU+GPU combo the CPU assigns difficult tasks to GPU and performs other tasks itself, thus saving much time.</p><h2>Here's a great video explaining the difference between a CPU and GPU:</h2><p>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.<br><br>On the other
side, there are fully-fledged servers as well which need some
installation and some modifications such as Amazon Web Services EC2.</p><h2>Data Structures and their use in Python</h2><p>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:<br><br>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)<br><br>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</p>
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