Complete roadmap to be a deep learning engineer
The field of deep learning has been growing exponentially in recent times, mostly due to the huge amount of data that is available and easily accessible computing resources, in this blog we are going to discuss what deep learning actually is, why to use deep learning, and how to get started with it so basically it will provide you the complete roadmap to be a deep learning engineer with books attached.
Table of Content
- What is Deep Learning?
- Why Deep Learning?
- How to get started with Deep learning
- Steps to be followed
- Books to be referred to (along with pdfs)
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What is deep learning?
“Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans”
Deep learning is a series of algorithms that attempts to imitate the learning of a human brain, or at least that’s what they claim, what actually happens inside is that it performs several complex mathematical functions that make it possible for a computer to perform a task like humans.
For example, we have seen driverless cars well deep learning is the key technology behind this miracle, we can talk to our phone and issue a command and it will do said task, eg Siri, Amazon Alexa, well deep learning makes all of this possible.
Through deep learning, a computer model learns to perform classification tasks directly from images, text, or audio. Deep learning algorithms can achieve state-of-the-art accuracy, even exceeding human-level performance from time to time.
Why learn Deep Learning?
State of the art performance: Speech recognition, Object detection, and image classification are some of the problems where traditional machine learning algorithms fails., but deep learning algorithm such as CNN, and RNNs performs exceptionally well due to their architectures.
Scales with data: As the data increases the classical machine learning algorithms’ performance plateaus, but deep learning algorithms’ performance increases as we add more data.
Less Feature Engineering: Classical machine learning algorithms often require complex feature engineering, after a deeper analysis of the dataset which makes it unappealing to most newbies that want to get their hands on machine learning, however, in deep learning, there is little to no feature and can learn from the data directly.
Transferable: One of the major advantages of deep learning is that we don’t really need to build everything from scratch, models trained for one problem can be used for other but related tasks, for example, in the computer vision domain, pre-trained image classification networks such as VGG16, ResNets, MobileNet, are often used as a feature extraction front-end for object detection and segmentation problems. The use of these pre-trained networks eases the model’s training and often helps achieve higher performance in a shorter period of time. This can be useful when we don’t exactly have a huge dataset and the computing resources are also limited.
How To Get Started With Deep Learning?
Getting started with deep learning can seem intimidating at first, with all the suggestions and advice floating around the internet, but it doesn’t have to be that way, we know starting with deep learning can be a headache when you don’t have an exact roadmap.
That’s the reason we have prepared a complete list of topics, and books as a beginner’s guide to learn deep learning.
1. Learn Python
You always need a language whenever you have to implement any logic or write any program. For that, python is the go-to language of most deep learning and machine learning practitioners because of its simplicity. It is a great choice as the first language to learn and there is no denying that it is extremely popular.
There are many machine learning frameworks like Tensorflow, Pytorch, Caffe, Theano, Keras, etc, Python makes your life easy when you are implementing complex deep learning algorithms like CNN and RNN.
Here is the book that you can refer to learn the basics of python.
Book1: Head First Python: A Beginner-friendly guide
Author: Paul Barry
Best For Beginners
This book will talk to you literally, one thing that separates this book from the rest is that it provides more of a visual format to engage our brain rather than using a syntax savvy approach that can become boring pretty fast.
Major Topics Covered
- Python fundamentals
- Working with built-in data structures
- Exception handling
- Data Wrangling
- Context managers,
- Decorators
- Comprehensions
- Generators
Book 2: Think Python: How To Think Like a Computer Scientist
Author: Allen B. Downy
Best For Beginners
The book is targeted toward individuals who have little to no programming experience, it provides hands-on coding exercises and in-depth explanations of each topic.
Major Topics Covered
- Variables, Expressions, and Statments
- Functions
- Recursion
- Data Structures
- Object Oriented Programming
2. Mathematics
Deep learning requires maths and lots of maths, but one need not be an expert in maths to delve into the realm of deep learning. If you have studied mathematics in your college you can start learning right now. Pretty much every course and tutorial that you find on the internet will require you to have a basic understanding of linear algebra and calculus, Here is are some of the books that I think are really helpful.
Book 1: Math for Deep Learning: What You Need to Know to Understand Neural Networks
Author: Ronald T. Kneusel
Best For Intermediate
if you need a little refresher you can refer to this book. The book expects you to have high school-level mathematical proficiency, it provides a sufficient mathematical foundation to get you started with deep learning.
Major Topics Covered
- Probability
- Statistics
- Linear Algebra
- Differential Calculus
- Matrix Calculus
- Backpropagation
Book 2: Mathematics for Machine Learning
Author : Macr Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
Best For Beginners
The basic mathematical knowledge needed to understand machine learning includes linear algebra etc. For those who are learning mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding.
Major Topics Covered
- Linear Algebra
- Vector Calculus
- Probability and statistics
- Matrix Decomposition
3. Neural Network Architecture
Now that you have come this far, we believe you are really serious about deep learning, its time to get your hands dirty, take one of the following books start practicing right now, don’t just read the book, implement the concept that you have learned using code and if possible do it using pure python ie without using any machine learning frameworks then only you will know how these frameworks work internally. Here are some of the books that I suggest.
Book 1: Deep Learning From Scratch: Building with python from first principles
Author: Seth Weidman
Best For Intermediate
The book starts with deep learning basics and moves quickly to the details of important advanced architectures, implementing everything from scratch. The books describe how neural networks work using a first-principles approach. You’ll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, you’ll be set up for success on all future deep learning projects.
Major Topics Covered
- Computation Graph
- Backpropagation
- Artificial Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
Book 2: Deep Learning with Python
Author: Francois Chollet
Best For Intermediate
Building a neural network from scratch is a great way to understand what’s going on underneath but neural networks can get complicated really fast and it will be a headache when you are working with real-life projects this is where we use frameworks like TensorFlow, PyTorch, Keras, etc. The book is written by Keras creator and Google AI researcher François Chollet, it builds your understanding through intuitive explanations and practical examples.
Major Topics Covered
- Deep Learning Fundamentals
- Computer Vision
- Sequence Models
- Generative Deep Learning
4. Deep Learning Specific libraries
Book 2: Deep Learning With Pytorch: Build, train, and tune neural networks using Python tools
Author: Eli Stevens, Luca Antiga, Thomas Viehman
Best For Beginners
This book teaches you to create deep learning and neural network systems with PyTorch. It gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated.
Major Topics Covered
- Tensors and Neural Networks
- Dataset and Data loaders
- Pytorch Modules
- Utilizing pre-trained models from PyTorch Hub
5. It’s the time to Build Projects
Learning theory can get so so far but to really master the art of deep learning one needs to get their hands dirty, so it is a must if one wishes to make a career in deep learning Following are a few project ideas that you can do and experiment.
- Handwritten Digit Recognition
- Cifar10 classification
- Sentiment analysis
- Sign digit Recognition
The above roadmap is for those who have little to no experience in machine learning, but if you are a little familiar with python and machine learning you can jump right into the deep learning part. The field of deep learning is so vast and growing that it is not possible to put everything in one article so it is the choice of the individual how far they want to go.
We believe that the Roadmap To Be A Deep Learning Engineer above will guide you in the right direction toward your journey in the mysterious realm of deep learning.
Also, we have prepared 28 days tutorial series to learn neural networks from scratch for beginners. You can follow the tutorials from the following link:
