Getting Started with Deep Learning with Python: A Complete Guide

. 5 min read

Today, we are surrounded by data. This is the age of information and now it has become a part of daily life. It is astonishing to see how the world has changed in the past few decades, and the luxury to exchange information, automate services, improve experiences and simplify living have become our priorities.

Consequently, the demand for Data Scientists and Analysts has been soaring to new heights. There is tremendous potential in this field and massive research work is underway all over the globe to fulfil the need for conversion of raw data into useful information. Artificial Intelligence has started reshaping the way things work around us.

How can you be a part of this extraordinary world of AI, Machine Learning and Deep Learning? Our guide to Deep Learning with Python is here to help.

Why Python?

Image credit: Stack Overflow

According to SlashData, there are around 8.2 million developers who use Python, which begs the question, why exactly is Python preferred by developers over other programming languages? The short answer is because of its simple syntax and readability. It is easily accessible by both programmers and non-programmers to test complex algorithms, and convert into language that is comprehensible.

Understanding Deep Learning  

Deep Learning is a very specific part of the broad spectrum that is Machine Learning, which in turn is an part of Artificial Intelligence. Although it can be confusing initially, Machine Learning is essentially the scientific study of numbers, statistics and algorithms that helps in making informed decisions based on structured data.

When we look at how learning occurs in a human brain, a complicated process involving neural nodes and connections is revealed. These nodes process information, and actually grow physical connections between them.

Diagram of an artificial neural network

Deep Learning attempts to imitate the working of the human brain. Using the principles of connected nodes, a machine mimics the ability, by categorizing data into various levels of Artificial Neural Networks (ANNs) and carries out the due process of learning through a non-linear approach. The objective: To solve problems with no pre-defined rules, assumptions and without human interventions.

Where is Deep Learning used?

There are several applications of Deep Learning, but its use is simply to break down any textual, imagery or verbally accumulated data and process it for insights without human intervention.

Some examples are:

  • Natural Language Processing - Computers can communicate with humans by reading and comprehending natural speech in human languages. Virtual assistants like Siri or Amazon Echo are excellent examples of NLP, as they process human language, and respond accordingly.
  • Image Recognition - By now you know that a machine is either trained or it learns on its own through recognition of patterns. However, in image recognition, the machine uses Convolutional Neural Networks (CNNs). CNNs break down images into numbers, recognise patterns and then identify the content.

The framework

1. Pre-requisites

Before stepping into the world of algorithm models and neural networks, you need to brush up on the basics of certain subjects.

  • Mathematics - Revise calculus, probability and linear algebra, since mathematics constitutes about 30% of deep learning.
  • Statistics - Have a clear idea about the various techniques and formulae of descriptive statistics and probability theory.

2. Prepare your gear

You'll need a good GPU, a robust processor and a minimum of 32GB of RAM.

Install Python, NumPy, SciPy, and matplotlib through the Anaconda distribution, a free and open-source distributor of programming languages like R and Python.

  • Also install Jupyter Notebook, an open-source software that allows live coding and data vizualization. Scikit-learn is a free library, specially meant for Python, that will help with regression testing, data mining and data analysis.
  • You should also install pip, the standard package management system for Python
  • Install packages. Theano, TensorFlow and Keras are a good place to start.

3. Python programming

To be able to implement the concepts of machine learning, you need to be able to code in Python. Python has several special libraries that are specifically targeted to machine learning.

4. Machine learning

Machine Learning is uses statistical methods to learn from structured data and help in decision-making processes. You will be exposed to the various types of Machine Learning, such as supervised, unsupervised, reinforcement learning, linear regression, overfitting, hyper-parameter, cost functions, under-fitting and so on.

5. Understand neural networks

Neural networks are complex entities that can have multiple layers, depending on the algorithmic models it is trying to predict for. You will learn about inputs, outputs, and neurons, among other aspects. Understanding how the data travels through the neural network, like how it receives input, performs calculations, and generates output, is critical to be able to develop models later on.

6. Venture into various Deep Learning models

Learn about the most popular Deep Learning models that can classify the input data. Some of these are:

  • Convolutional neural networks
  • Deep belief networks
  • Recurrent neural networks
  • Auto-encoders
  • Generative adversarial networks

7. Learn to train your neural networks

Here you will encounter network architecture, output channels, hyper-parameters and other characteristics. Training also depends on the number of layers that the network has. The more layers a network consists of, the longer it takes to train. You will train it to deliver output without external intervention. You should also train them to learn from past experience and create layer over layer automatically.

8. Learn computational graphs

By now you have wrangled the data, but you still need to communicate insights to people who will have little to no background in programming and analytics. That's where computational graphs come in. They are a way of expressing and visualizing mathematical expressions through simplified graphs that can be easily understood by non-technical associates.

9. Get hands on with applications

As a final step, try building deep learning networks through frameworks like Theano, TensorFlow and Keras. They are open-source frameworks that help to define, transform and calculate mathematical expressions with multi-layered arrays. You can build over these and construct complex deep learning projects. If you want to learn something new, you need to get down into the field and not just function on theoretical knowledge. Practising would only lead your way.


So, these are the steps to learn Deep Learning with Python. Refer to videos, blogs, white papers and tutorials, all of which are easily available online.

But if you really want a career transition into machine learning, a structured program is the way to go. Programs guided by industry experts will expose you to real world applications of your lessons, and you will see your studies taking concrete shape sooner.

GreyAtom not only has industry mentor-led programs with constantly revised content, our learning platform is complete in of itself. There are lessons alongside coding environments, where you can code as you go, solidifying your knowledge. Check out our full stable of programs, and speak to a counsellor today to get more information.

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