Learning Path for Deep Learning in 2021

Learning Path for Deep Learning in 2021
Learning Path for Deep Learning in 2021

In this article, we are going to know about the learning path for Deep Learning in 2021.

What to learn and explore about Deep Learning? Want to know which concepts are needed to become master of Deep Learning? You are at the right place!!

In this article we will show u the concepts you should learn for deep learning, also at the end some tips to ace your Interviews in this field.

Excited? Let’s get started.

Table of Contents

In this article on the learning path for Deep Learning in 2021, let’s see what we are going to learn.

  • Introduction
  • Path for Deep learning 
  • Points to remember before going for interviews
  • Conclusion

Introduction

Deep learning is part of the Machine Learning (ML) field which is based on Artificial Neural Networks (ANNs), which is used to extract high-level features of data by multiple layers of processing. Because of this, there are many things that you can start learning because the field is way vast than you imagine. Like every other field of Artificial Intelligence (A.I), this field is also getting in demand and the number of jobs in this field is also increasing every year.

Path for Deep Learning 

Firstly, you should start making a schedule and start managing time for the lesson you will study every day. Remember that don’t start rushing and finishing concepts in hurry, take your own time to understand each and every concept that you will encounter. Make sure that you sit for studying when your mind is in peace and calm. 

Here are the concepts that you should start for deep learning as a beginner to advance:

  1. Mathematics
  2. Python/R/Java
  3. Introduction to Machine Learning 
  4. Convolution Neural Networks (CNNs)
  5. Sequence Models
  6. Natural Language Processing (NLP)
  7. Generative Adversarial Networks (GANs)
  8. Data Science 
  9. Projects

1. Mathematics

Mathematics is the most essential requirement that you will see in every field of Machine Learning (ML) and Artificial Intelligence (A.I). Mathematics concepts like Probability, Statistics, Linear Algebra, and Calculus. Start practicing and solving problems based on these topics before you jump into another step.

2. Python/R/Java

Programming language is an important and basic part of any engineer’s life.

You need to be an expert in any one of the programming languages if you want to survive as an engineer. Programming Languages like Python, R, Java are the most popular in today’s world. As for me, I would recommend you to become an expert in python language as it is fast, easier to understand, easier to code, and flexible due to its vast kinds of libraries which are useful in Machine Learning and Artificial Intelligence. Start Learning and making projects in python because it will become useful ahead. Techlearn also has some free resources which are useful resource to Python programming. You can also attend live sessions on TechLearn.live for various python projects that you can showcase in your resume.

Also Read: 8 Open-Source Python libraries you need to know

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3. Introduction to Machine Learning

As Deep Learning is part of Machine Learning (ML), you should be familiar with the basics of Machine Learning. Start with linear and logistics regression and start solving problems in it. 

DO READ: Deploying your first Machine Learning project in python

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4. Convolution Neural Networks (CNNs)

Convolution Neural Networks (CNNs), also known as artificial neural networks (ANNs) relays on training data to learn and improve their accuracy over time. It is the heart of Deep Learning Algorithms. If the algorithms are fine-tuned, they are powerful tools in Artificial Intelligence (A.I). Python and R can also be useful in Neural Networks because of their libraries and packages.

Also Read: Understanding Neural Network

5. Sequence Models

After completing Convolution Neural Networks (CNNs), you should start with sequence models. These include Recurrent Neural Networks (RNNs) which are used for image captioning, Time Series Prediction, Natural Language Processing (NLP), Machine Translation and many more. Also, Long Short-Term Memory (LSTMs), and Gated Recurrent Unit (GRU) are included. Long Short-Term Memory (LSTMs) is mainly used for Deep Learning that has feedback connections it processes an entire sequence of data like speech and videos. Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks and it is like Long Short-Term Memory (LSTMs) but lacks an output gate.

6. Natural Language Processing (NLP)

Natural Language Processing (NLP) is part of Artificial Intelligence (A.I). It gives the computers the ability to understand the text and spoken words. The best example is Google Assistant which uses this technology. 

It is an important part of Deep Learning, and you should study it carefully and start searching for some projects in it. You will learn various NLP concepts like embeddings and attention models.

Also Read: Getting Started with Natural Language Processing for Beginners

7. Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) have been the most important part in these recent years. Many Artificial Intelligence (A.I) projects have GANs as the main concept. It is mainly used for training generative models. If you master this concept, then I am sure you will be on a different level in the field of Deep Learning.

8. Data Science

When it comes to models, Data Science is the one that comes to everyone’s mind. Data Science is a vast field on its own and gaining this knowledge will give u an advancement in your career. You can Start with Data analysis, Big Data Analysis, Data Visualization, Data Mining, and much more. First, just start with one of these and think of specializing it.

Also Read: How can you kickstart your career in Data Science with python?

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9. Projects

Now if you are at this step then it’s great!!! You have completed your Deep Learning course. But the important part remains, making and showcasing your projects. Making Projects is an important thing as it shows the knowledge you have gained by learning something and your skillset. Some of the projects you can work on are Face Detection, Classifying Datasets, Compose Music with Recurrent Neural Networks, Using Neural Networks to rate a selfie, color gradient Black & White pictures using Deep Learning, sentiment analysis using RNNs and CNNs, and many more. You can also try to make a new project of your own and miracles in your life.

You can start your Deep Learning Course at Techlearn :

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Points to remember before going for interviews

When it comes to the part of interviews, even if you have mastered Deep Learning, you should not be overconfident. Make your resume and your LinkedIn profile strong by making projects and posting them on social media. Don’t forget that if someone like a client or interviewer tells you to explain your project, make sure that you present them and explain them in a layman type. You should be confident enough to answer all of their questions. Remember that after learning Deep Learning, don’t stop instead start learning every neighboring field as possible because the more skills you have, the chance for you to take the job is high. Also, your more skills will you a chance at a higher post with a high salary.

Conclusion

So, that’s all in this tutorial of the learning path for Deep Learning in 2021.

Congrats on completing the course!! You have mastered it, just start exploring more and start learning. You can also check our site Techlearn.live where you can find various Blogs, Live Courses, and Free live sessions on different kinds of projects in Python, Machine Learning, Artificial Intelligence, and much more. Thank You.

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