How to Learn Deep Learning from Scratch?

The Ultimate Guide for Learning Deep Learning from Scratch, for Beginners in the Artificial Intelligence Domain| ProjectPro

How to Learn Deep Learning from Scratch?
 |  BY Manika

Are you juggling between various terms related to Deep Learning, like convolutional neural networks, pooling layer, backpropagation algorithm, etc., and are not able to make sense out of them? All your worries end here. Read this article on how to learn Deep learning with Python from scratch. 


Deep Learning Project for Beginners with Source Code Part 1

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The popularity of machine learning and deep learning concepts in solving real-world problems is increasing daily. As per Earthweb, about 48% of businesses utilize AI-based technologies to leverage large datasets. A quick search on LinkedIn reveals that there are about 41K+ jobs that list deep learning as a skill. According to financesonline.com, the deep learning market is expected to grow to $1 billion by 2025. Deep Learning is the future, and if you are still in doubt about whether you should consider starting a career in deep learning, then you are on the right page. Read this article to learn how to kickstart a deep learning career from scratch.

Job Statistics for deep learning engineer role

Should I learn Deep Learning?

We have already highlighted the search results on LinkedIn for deep learning jobs. Besides that, you must look at the salaries of a deep learning engineer. According to Indeed.com, a deep learning engineer in the US is paid about $131669 per year. 

Should I learn deep learning

The companies that hire deep learning engineers include Google, Microsoft, Tesla, Amazon, Accenture, etc. As per financesonline.com, the US deep learning market is estimated to be worth $80 million by 2025. All these numbers suggest Deep Learning engineers are likely to be in demand even in the future. Therefore, we recommend you move toward the next section, which explores the prerequisites for starting a career in deep learning.

Get Started with Deep Learning

To get started with a career in Deep Learning, an individual is expected to possess the following skills:

  • Basic understanding of a programming language like Python/R/Scala.

  • Since most deep learning concepts are mathematically rigorous, you must have a strong foundation in advanced mathematical concepts.

  • In-depth knowledge of building and deploying machine learning projects.

  • Ability to convert a data science problem into a deep learning problem.

  • Practical experience with implementing projects on image processing and computer vision.

  • Good communication skills to convey the results of a deep learning based solution.

Beginner in Deep learning

Now that you clearly understand the skills required for pursuing a career in deep learning, let us move ahead with the resources for learning deep learning.

Where to Learn the Math for Deep Learning?

To become a deep learning engineer, ensure you have polished your mathematical skills well. 

Maths for Deep Learning

In case you haven’t and are looking for resources to learn math (probability, statistics, differential equations, calculus, and linear algebra) for deep learning, check out the books and topics mentioned below.

Topic

Recommended Textbooks

  • Linear Algebra

  • Probability and Statistics


  • Differential Equations


  • Advanced Calculus

The books mentioned in the table are all beginner-friendly and explain the concepts in detail. Before pondering over the next section, you should enhance your skills by solving as many textbook exercises as possible.

Practice makes a man perfect! Start working on these projects in data science using Python and excel in your data science career.

How to Learn the Basic Concepts of Deep Learning?

Besides foundational knowledge in mathematics, a deep learning expert must possess good programming skills and a decent understanding of all the concepts in machine learning algorithms. That’s because machine learning models are less complicated than deep learning algorithms.

Basics of Deep Learning

For basic programming skills, we suggest you learn Python. A book recommendation for that is Python Programming for the Absolute Beginner by Michael Dawson. This book contains exciting games and fun exercises to make learning enjoyable. It will guide you in implementing loops, various data types of variables, statements, OOPs, and data file handling in Python. It also contains a chapter on sound, animation, and program development in Python.

Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and ‎Shai Ben-David is a good pick for learning about machine learning models. The book starts with the basics of mathematics and then explains the machine learning models in detail. One of the best things about this book is that it gradually builds up the momentum from theory to practice. Another key feature is that it attempts to explain advanced theoretical concepts.

Now that you are familiar with the prerequisites of learning deep learning, you must proceed to the next section that will guide you through the learning process of understanding deep learning.

How to Learn Deep Learning Step-by-Step?

Here is your 3-step guide for learning Deep Learning.

How to learn Deep learning

The implementation of deep learning techniques requires the utilization of a GPU. And to gain free GPU access, you don’t need to install anything on your system, as multiple options for cloud computing resources are available. If you are a beginner, we recommend you use Google collaboratory as one can access it for free. Once you are comfortable with the workflow of implementing a deep learning algorithm, you can look for paid alternatives like Paperspace gradients, AWS EC2, etc., that offer better services than Google colab.

Many popular deep learning models have been developed due to the hard work of researchers working at top universities and companies worldwide.  They have published their research in popular journals, and you must read their work to understand the practical deep learning models deeply. Besides the research papers, you must go through this popular deep learning book by Ian Goodfellow and Yoshua Bengio, and Aaron Courville that you can download for free.

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Knowledge without action is incomplete. So, you must implement what you have learned from various textbooks and deep learning online courses. In the beginning, completing a single project might be challenging, but as you continue solving more complex problems, you will be more prepared to solve a practical deep learning problem.

If you are worried about finding a resource for the last step, you must quickly calm down, as the next section will discuss the perfect solution for learning how industry experts implement deep learning projects.

Recommended Reading: 100 Deep Learning Interview Questions and Answers for 2023 

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Learn How to Create Deep Learning Algorithms in Python!

Building projects in data science that utilize deep learning techniques will be a piece of cake for you if you try out the below-mentioned industry applications of deep learning models.

Deep Learning models in Python

This project aims to predict individuals’ income class (>50K or <=50K) based on their personal information such as age, education, etc. You will work on a dataset that contains 48,842 rows and has missing values. You will learn how to perform exploratory data analysis and handle null values and outliers. You will also learn to draw insightful plots using Python’s libraries- seaborn and matplotlib. Furthermore, the project solution will guide you on using the vanilla deep neural network to predict the income class.

Source Code: Census Income Data Set Project 

This project aims to classify t-shirt images into plain or topographic. The dataset comprises 850 images, where 600 images are for testing, 100 for training, and 150 for validation. You will use the testing and training images to help the machine learn the pattern and the validation set for analyzing the model's performance. This project will guide you on Deep learning and convolutional neural networks in a beginner-friendly way. You will also learn about data visualization, transfer learning, and implementing the two deep learning models: Inception and Resnet.

Source Code: Tensorflow Transfer Learning Model for Image Classification

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This project aims to classify the images in the dataset into three classes- social security, driving license, and others. This project will discuss the different layers in a convolutional neural network- pooling, flatten, convolution, etc.

Image Classification Deep Learning Project

You will learn about implementing different activation functions: Sigmoid, Leaky RelU, RelU, and Step function. You will also know the difference between multiclass and multilabel classification. Lastly, the project will also teach you about optimizers, loss functions, and data augmentation.

Source Code: Build a Multi Class Image Classification Model Python using CNN 

This project aims to classify the customer review in the range of 1-5 by utilizing the Gated Recurrent Unit (GRU) model. 

Analyzing Customer Reviews using GRU

You will learn about the basics of the GRU and the Recurrent Neural Network model. Additionally, the project will compare the two models and use NLP techniques like lemmatization, tokenization, etc. You will also learn how to deploy the complete solution using Flask.

Source Code: Build a Review Classification Model using Gated Recurrent Unit

This project aims to forecast the time series data using the Long Short term memory neural network. 

Time Forecasting Deep Learning Project

Besides the LSTM model, the project will introduce you to deep learning algorithms like perceptron, deep neural networks, recurrent neural networks, convolutional neural networks, and the Boltzmann network. You will learn to pre-process the data before passing it as an input to a deep learning algorithm.

Source Code: Time Series Forecasting with LSTM Neural Network Python

Use these R projects for practice of R programming lanaguage and learn data science today!

This project aims to build an image-to-image translation model using Cycle GAN in PyTorch. It will discuss the architecture of Cycle GAN in detail and the shortcomings of deep neural networks. You will learn about the ResNet model and its comparison with deep neural networks. This project will briefly explain the cycle loss function and the Patch GAN loss. Lastly, it will teach you about reflection padding and instant normalization.

Source Code: CycleGAN Implementation for Image-To-Image Translation 

This project aims to build an early fire detection system with the help of the Mask R-CNN model. It will explain image localization, image detection, and image localization in detail. It will help you understand the basics of the Regional Proportional Network, Bounding box regressor, and ROI classifier. Additionally, you will learn about the Mask RCNN model and the popularly used MS Coco dataset. You will also learn how to annotate images for preparing the training dataset. The project will also guide you in understanding how to prepare and store log files.

Source Code: Image Segmentation using Mask R-CNN with Tensorflow 

This project aims to explain the basics of computer vision and is best suited for newbies in the deep learning domain. It’ll teach you how images are considered as matrices of real numbers and how arithmetic operations like subtraction and addition are performed on those matrices. You will learn how to detect edges in an image using the OpenCV library of Python. Additionally, the project will guide you in understanding template matching, Hough Transformation, and video processing.

Source Code: OpenCV Project for Beginners to Learn Computer Vision Basics 

This project aims to solve a multi-class classification problem using a state-of-the-art BERT model. This project will introduce you to the basics of Natural Language Processing, like Bag-of-words and TF-IDF models. You will also learn about word embedding techniques like Word2Vec, FastText, and Glove. Furthermore, the project will explain in detail the attention mechanism and the utilization of the BERT model.

Source Code: Multi-Class Text Classification with Deep Learning using BERT

This project aims to detect text in images using convolutional and recurrent neural networks. It will teach you the basics of convolutional and recurrent neural networks. You will learn about the CRNN architecture and the CTC loss function. Additionally, you will learn about pickle and CSV files. The project solution will also explain how to interpret the results of the algorithm implementation.

Source Code:  Deep Learning Project for Text Detection in Images using Python 

What is the Best Way to Learn Deep Learning?

The best way to learn deep learning is by implementing successful machine learning projects on real-world-inspired datasets. You are incorrect if you think that we will come up with a long list of deep learning courses that you must pursue. Allow us to correct you with ProjectPro’s solved end-to-end projects in deep learning.  

The best way to learn Deep Learning

ProjectPro hosts about 250+ solved projects in Data Science and Big Data that professional experts in the two domains have prepared. The project solutions are in the form of videos you can browse through from anywhere in the world. So, what are you waiting for? Gain access to these projects and excel in your job today!

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FAQs on Learn Deep Learning

1) Can you learn deep learning on your own?

Yes, you can learn deep learning on your own if you are learning it from the right resources. Check out ProjectPro if you are looking for a one-stop solution for deep learning resources.

2) Is deep learning easy to learn?

Yes, deep learning is easy-to-learn, provided you fulfill its prerequisites. The prerequisites include advanced mathematical and programming skills.

3) How long Does it take to Learn Deep Learning?

The time required to learn deep learning depends on an individual’s background. However, if you are a beginner and are starting to learn deep learning from scratch, it’ll take about six months. 

4) How do I learn deep learning without machine learning?

Learning deep learning without machine learning is not recommended as it makes the learning process smooth. However, you can still choose to learn deep learning; it’ll only take longer.

 

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About the Author

Manika

Manika Nagpal is a versatile professional with a strong background in both Physics and Data Science. As a Senior Analyst at ProjectPro, she leverages her expertise in data science and writing to create engaging and insightful blogs that help businesses and individuals stay up-to-date with the

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