Deep Learning

“Dive into the world of Deep Learning, where machines emulate human brain functions to understand complex data. Discover easy-to-follow tutorials, practical insights, and real-world applications in this exciting field of artificial intelligence!”

What is Deep Learning? 

Deep learning is a type of machine learning that learns to represent the world through a nested hierarchy of concepts. Unlike traditional machine learning, where feature extraction and classification are separate, deep learning models take care of both feature extraction and classification themselves. The model builds simpler concepts and representations from the data, which are combined to form more complex features.

In essence, deep learning breaks down complex concepts into simpler ones and builds up its hierarchy of concepts using the training data. Deep learning models require large amounts of data and significant computational power to function effectively.

Applications of Deep Learning

Deep Learning is used in various use cases because of its wide range of capabilities. Some key areas where Deep Learning is used are:

1. Computer Vision: Deep Learning is used in facial recognition, object detection, and image classification. Facial recognition is used in the mobile industry for face unlock. Object detection is used in anti theft or detecting mobile phones during examinations. Image classification is used in medical imaging, satellite images, traffic control systems, brake light detection. 
2. NLP (Natural Language Processing) – This has paved the way for recent LLMs. Hence knowing NLP plays a vital role for the future. Some applications of NLP include chatbots, language translation, and sentiment analysis. Chatbots are used as customer support, language translation is used in mobile applications, translation apps. Sentiment analysis is used in social media applications to sensor harmful media and also used in knowing sentiment of a customer’s comment and act based on it. 
3. Recommendation Systems – We all know that recommendation systems play a vital role in business. Almost every company wants to keep their customers engaged. Some examples include recommendation of reels in instagram, recommendation of movies in netflix, recommendation of songs in spotify. Hence know and understanding recommendation systems will help in building and expanding business. 

ML vs DL vs RL vs GenAI 

Aspect	Machine Learning (ML)	Deep Learning (DL)	Reinforcement Learning (RL)	Generative AI (GenAI)
Definition	ML involves using algorithms to analyze data and make predictions based on past experiences.	DL is a subset of ML where models automatically learn representations (features) through hierarchical layers.	RL is a type of ML where an agent learns by interacting with an environment and receiving feedback (rewards or penalties).	GenAI uses AI models to generate new content, like text, images, or music, based on learned patterns.
Feature Engineering	Data scientists manually build features for the model to analyze.	Features are learned automatically from the data by the model.	No specific feature engineering; the agent learns based on environment interactions.	Content generation does not require manual feature extraction as it builds patterns based on data.
Data Requirements	Can plateau in performance with more data.	Performance improves as more data is provided.	Requires continuous feedback for learning from the environment.	Requires large datasets to generate accurate and creative content.
Computational Power	Less demanding computationally.	Requires significant computational power, often GPUs or TPUs.	Computational requirements depend on the complexity of the environment and feedback system.	High computational power is needed, especially for training large models like GPT or image generators.
Interpretability	Easier to interpret, especially with models like decision trees.	Difficult to interpret due to the complexity of learned representations.	Can be hard to interpret since it relies on feedback loops and trial-and-error learning.	Interpretability is limited as the model generates content based on complex learned patterns.
Popular Use Cases	Predictive modeling, classification, regression tasks.	Image recognition, natural language processing, autonomous systems.	Robotics, game AI, real-world decision making.	Text generation (e.g., ChatGPT), image creation (e.g., DALL-E), music composition.

Key Differences:

• ML: Involves manual feature engineering, suitable for tasks with structured data and can plateau with data.
• DL: Automatically learns hierarchical features, benefits from large data, but is computationally heavy and less interpretable.
• RL: Focuses on learning through trial and error with feedback, commonly used in dynamic environments.
• GenAI: Specializes in generating new content using patterns from large datasets, often requiring immense computational resources.

Skills Required:

1. Fundamentals of Machine Learning:
   • Deep learning is a machine learning type, so understanding machine learning fundamentals is crucial.
   • This includes knowledge of supervised and unsupervised learning and a strong grasp of basic algorithms.
2. Mathematics and Statistics:
   • Since deep learning models involve complex mathematical computations, understanding linear algebra, calculus, probability, and statistics is important.
3. Programming Skills:
   • Being able to code in programming languages like Python is essential to implement deep learning models and frameworks.
   • Familiarity with Python libraries such as TensorFlow, PyTorch, or Keras is also beneficial.
4. Understanding of Neural Networks:
   • A deep learning model works by breaking down complex concepts into simpler representations using layers in neural networks, so knowledge of how these neural networks function is important.

Learning Path to Learn Deep Learning:

1. Prerequisite Knowledge:
   • Start with understanding fundamentals of machine learning as a foundation.
   • Learn basic mathematics (linear algebra, calculus, probability) that supports deep learning algorithms.
2. Study Neural Networks:
   • Learn how deep learning algorithms work by understanding how they build features from data and create hierarchical representations.
3. Hands-on Projects:
   • Once the basics are understood, work on practical problems such as image classification or object detection using libraries like TensorFlow or PyTorch.
   • Implement deep learning models to gain practical experience.
4. Explore Computational Resources:
   • Since deep learning requires significant computational power, gaining hands-on experience using GPUs and TPUs is important for training large models.

Career Options in Deep Learning 

Some career options in Deep Learning include the following:

1. Deep learning Engineer – In this role a person would typically develop,  train, and optimize deep learning models using frameworks like TensorFlow, PyTorch, and Keras. The person would also have a firm understanding of neural networks and build custom neural networks if needed.
2. AI Research Scientist – In this role a person would keep himself updated on cutting edge technologies. Here you can think yourself researching on the ideas and implementing them to improve the efficiency of the current existing models or create new ones. Lot of the time would be spent on building new architectures, running experiments and finding new possibilities. This person should be in love with deep learning algorithms. 
3. Computer Vision Engineer – This role solely focuses on Vision capabilities of Deep Learning models. Some popular vision applications include autonomous vehicles, facial recognition and segmentation of agricultural fields using satellite imagery. It basically helps machines see the world and understand, and help machines find difficult patterns. 
4. Natural Language Processing Specialist – This role will be for people who want to learn how machines understand language. You would be building systems which understand natural language, interpret them and also generate or respond in the natural language aswell. Some real world applications you will be working on would be building chatbots which can understand what you query and respond like a human would. Understand the query like a human would, and build a response from different resources. Breakthrough in NLP led to Language Models and LLMs. These models can understand natural language and are based on NLP. 

Salary Trends in Deep Learning

Ambition Box – Deep Learning Engineer salary in India with less than 1 year of experience to 6 years ranges from ₹ 3.0 Lakhs to ₹ 24.0 Lakhs with an average annual salary of ₹ 11.5 Lakhs based on 362 latest salaries.

GlassDoor The average salary for a Deep Learning Engineer is ₹10,14,740 per year in India.

run.ai In United States the base salary ranges from $129,029 to $171,587, with an average base salary of $149,409.

Common Frameworks for Deep Learning:

1. PyTorch:
   • Developed by the AI Research Group of Facebook.
   • Initially released in 2016.
   • Python-based library, written in C and Python.
   • Popular among researchers due to its flexibility for deep learning development.
   • Supports GPU to parallelize computations, which reduces training time and improves performance.
2. TensorFlow:
   • Developed by Google Brain Team, released in 2015.
   • One of its key features is “TensorBoard,” which is very effective for data visualization.
   • Written in C++ and Python.
   • Earlier versions required more programming experience, but recent releases have simplified the coding process.
   • It also has a large community base and offers strong support for training and visualizing models.
3. Keras:
   • A high-level library that focuses on enabling faster experimentation.
   • Built on top of TensorFlow, it allows for quick implementation of ideas without focusing much on coding details.
   • Ideal for those starting their deep learning journey due to its simplicity.
   • Keras is used for easier and faster neural network implementation compared to lower-level frameworks.

These frameworks help make the process of building and training deep learning models more efficient by supporting parallel computations and reducing code complexity.

Projects in Deep Learning

Below mentioned projects are some industry standard projects which helps you understand the fundamentals of Deep learning and also build solutions using deep learning. 

1. Plant Disease Classification using AlexNet – Identification and classification of plant disease at the leaf level is a crucial part of crop management and is important in the early detection and diagnosis of plant diseases. Hence in this project we use AlexNet a transfer learning which is known for its ability to handle large-scale image recognition tasks. 
2. Computer Vision to Detect License Number Plate – We will learn how to detect a number plate of a car and extract its values using computer vision. We are going to use the OpenCV library of Computer vision to detect the number plate of cars and the pytesseract library of deep learning to read image types and fetch characters and digits from the number plates. Finally, we build a graphical user interface using Tkinter to display our project’s work.
3. Stock Price Prediction using LSTM and its Implementation – we’ll use LSTMs and explore how a machine learning algorithm can be used to potentially predict stock prices, along with the exciting possibilities and important considerations to keep in mind.
4. Build Image Caption Generator using Deep Learning – You will learn about an image to caption generator and how it uses deep learning to create captions for images. We will explain what image captioning is and how it can help describe pictures better.
5. Sentiment Analysis with NLP & Deep Learning – Helps you understand how to use NLP with a use case. Goal is to know what users are saying about products and services. It can help in future decision-making. 

Deep Learning Books 

• Deep Learning From Scratch: Building with Python from First Principles by Seth Weidman published by O’Reilly
• Deep learning in Python/ Pytorch by Manning Publications
• Grokking Deep Learning by Andrew W. Trask published by Manning Publications
• Hands-on Machine Learning with Scikit-learn Keras and TensorFlow by Aurelion Geron published by O` Reilley
• Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville published by MIT Press

Find more books and explore their content with our blog on Top 13 Must-Have Books for Deep Learning!

Deep Learning Interview Questions

1. What is the difference between a Perceptron and Logistic Regression?
2. Can we have the same bias for all neurons in a hidden layer?
3. What happens if we don’t use activation functions in a neural network?
4. What happens if all weights are initialized with the same value in a neural network?
5. List some supervised and unsupervised tasks in Deep Learning.
6. What is the role of weights and bias in a neural network?
7. How do forward propagation and backpropagation work in deep learning?
8. What are the common data structures used in Deep Learning?
9. Why should we use Batch Normalization?
10. What activation functions have you used, and how do you choose one?

You can read our article on Deep Learning Interview Questions to find answers for these questions. 

Frequently Asked Questions

Q1: What are examples of deep learning?
A: Image recognition, natural language processing, and predictive analytics are examples. It involves neural networks and large datasets for complex tasks.

Q2: Is ChatGPT deep learning?
A: Yes, ChatGPT is a large language model, a type of deep learning system, trained on vast text data to generate human-like responses.

Q3: What is the difference between ML and deep learning?
A: ML uses algorithms to learn from data, while deep learning uses neural networks, a subset of ML, for more complex tasks and larger datasets.

Q4: Why is it called deep learning?
A: It refers to the multiple layers of neural networks, creating a ‘deep’ structure for learning complex patterns and representations.

Q5: What is the concept of deep learning?
A: Deep learning involves training artificial neural networks to learn and make decisions, mimicking the human brain’s learning process for complex tasks.