Deep Learning vs. Neural Networks: What’s the Difference?

Neural or artificial neural networks (ANNs) are a subset of machine learning and fundamental to deep learning. They are inspired by the way our brains work. Just like our brains are made up of interconnected neurons, neural networks consist of artificial neurons called "nodes" or "units" that work together to process information. Each node is like a tiny worker that receives some input, performs a simple calculation on it, and passes the result to other nodes.

Here's an analogy to help you understand: Imagine you're trying to decide whether to go outside or stay indoors based on the weather. You might consider factors like temperature, humidity, and whether it's sunny or rainy. Similarly, a neural network takes in a bunch of inputs, such as the brightness of an image or the time of day, and combines them to make a decision.

To make these decisions, neural networks go through a learning process. They are trained on a large set of examples. For example, a neural network may be trained to recognize cats in pictures by looking at thousands of cat images. By repeatedly adjusting the strengths of the connections between nodes, the network learns to recognize patterns and make accurate predictions.

There are various applications for neural networks, such as forecasting, marketing research, fraud detection, risk assessment, etc. Among them, the most well-known neural network is Google’s search algorithm.
 

Applications of Neural Networks

It has applications in key sectors, including finance, healthcare, and automotive. Here is a list of some of the top such applications:

• Social Media: The ‘People you may know’ feature suggests people you might know so that you can send them friend requests. This feature is implemented using Artificial Neural Networks. Another application is facial recognition, which is done by finding reference points on the person’s face and then matching them with an already available database using convolutional neural networks.
   
• Personal Assistants: Siri, Alexa, Cortana, etc., are personal assistants and examples of speech recognition that use Natural Language Processing to interact with users and formulate a response accordingly. It uses artificial neural networks to handle tasks such as managing the language syntax, semantics, correct speech, conversations, etc.
   
• Signature Verification and Handwriting Analysis: Artificial Neural Networks (ANNs) are used to verify signatures. It is trained to recognize the difference between real and forged signatures. Also, it can be used to verify both offline and online signatures.

Deep learning is a subset of Machine Learning (ML) in Artificial Intelligence (AI). Deep learning uses neural networks to process data such as images, videos, or text. Deep learning models are built with layers of artificial neurons called artificial neural networks (ANN). These networks are designed to process information and learn patterns from vast amounts of data.

Deep learning models are trained by providing them with many examples and letting them figure out the underlying patterns independently. Once trained, the deep learning model can take in new, unseen data and make predictions or classifications based on what it has learned. The power of deep learning is in its ability to automatically discover complex patterns and relationships in data without explicitly being programmed for every specific task.

It uses multiple layers of neural networks to make intelligent predictions or decisions. It's like teaching a computer to think and learn on its own by showing it many examples and letting it figure things out.

Deep learning systems learn from data representations. It can learn from unstructured or unlabeled data. Through deep learning, the abilities of computer systems are improved to categorize, identify, detect, and define data. It plays a major role in today’s technologies, such as digital assistants, voice-enabled TV remotes, credit card fraud detection, and emerging technologies like self-driving cars.
 

Applications of Deep Learning

Today, deep learning has applications across different industries for various purposes. Here are some top applications:

• Speech recognition: All major speech recognition systems like Microsoft Cortana, Alexa, Google Assistant, and Apple’s Siri are based on deep learning.
   
• Natural Language Processing (NLP): NLP is about teaching computers to understand and process human language. Deep learning models can be used to build chatbots, virtual assistants, and language translation systems.
   
• Pattern recognition: In medical diagnosis, it can give more accurate results than the human eye.
   
• Recommender systems: Today, deep learning is used to study user choices across various domains, and one such example is Netflix.

Researchers are constantly working on new technologies based on neural networks. Due to an increase in new technologies, there is huge career growth in the field of neural networks.

Here is a list of top skills required for a successful neural networks career -

• Knowledge of Applied Math
   
  Neural networks rely heavily on mathematics, particularly linear algebra and calculus. Think of linear algebra as a way to handle and manipulate data efficiently and calculus as a tool to optimize and improve the performance of neural networks. Understanding these mathematical concepts will help you build and train effective neural networks.
   
• Fundamental Programming Skills
   
  You'll need to learn how to program to work with neural networks. Programming languages like Python are commonly used in the field of AI. It's like learning a new language that allows you to communicate with the neural network, tell it what to do, and analyze its outputs. It involves being well-versed in the following programming languages.
  
  
  • R
  • Python
  • Java
     
• Distributed Computing
   
  Using multiple computers or processors to work together on a neural network task is distributed computing. This helps speed up the training processes. By harnessing the power of multiple computers, distributed computing skills allow neural networks to process large amounts of data and solve complex problems more quickly and efficiently.
   
• Data Modeling and Evaluation
   
  Data Modeling involves preparing and organizing data to train the network. This includes cleaning and transforming the data to make it suitable for the network's input requirements. Evaluation skills for neural networks involve assessing how well the trained network performs on new, unseen data. This is done by comparing the network's predictions to the actual outcomes and calculating metrics like accuracy or error.
   
• Software Engineering
   
  This involves designing, developing, and maintaining the software infrastructure required to effectively implement and deploy neural networks. Skills such as coding, debugging, and software architecture are essential for building robust and scalable neural network systems. 

Those focusing on a career in deep learning should focus on the algorithms and latest technologies required for deep neural network systems. It includes algorithms, data analytics applications and big data technologies, ML and decision-making algorithms, digital signal processing principles, python programming languages, etc. 

Here is a list of top skills required for a successful deep learning career -

• Computer Science Fundamentals
   
  Deep learning requires an understanding of the fundamentals of computer science, like data structures, algorithms, and programming languages. Data structures organize and store information efficiently. Algorithms are step-by-step instructions for solving problems. Programming languages are used to write code that instructs computers. Deep learning combines these fundamentals to build powerful models to learn and make predictions from complex data.
   
• Math Skills
   
  Some math concepts help you understand how deep learning and machine learning algorithms work and act as the foundational block. The three most important concepts you should learn in math are
   
  
  • Probability & Statistics,
  • Linear Algebra,
  • Calculus.
     
• Programming Skills
   
  Deep learning involves a lot of coding. You should have excellent programming skills, which are commonly used in deep learning frameworks. You'll need to write code to build and train deep-learning models. The most used programming languages in deep learning are
   
  
  • Python
  • R
  • C
  • Java
     
• Neural Networks
   
  Deep learning is based on neural networks, so understanding how they work is essential. Neural networks consist of interconnected layers of artificial neurons that process and learn from data. Learning the basics of neural network architecture, such as input and output layers, hidden layers, and activation functions, is crucial.
   
• Deep Learning Frameworks
   
  Deep learning frameworks provide tools and libraries that simplify the implementation of deep learning models. Popular frameworks like TensorFlow and PyTorch allow you to efficiently build, train, and deploy deep learning models. Being familiar with these frameworks and their functionalities can help you work effectively in the field.
   
• Machine Learning Expertise
   
  Machine learning skills for deep learning involve understanding and applying complex algorithms to train computers to learn and make predictions from large amounts of data. It requires knowledge of neural networks, optimization techniques, and programming languages like Python.
   
• Soft Skills
   
  Even though deep learning is a technical field, soft skills are nevertheless vital. So you must have soft skills like:
  
  
  • Communication
  • Time management
  • Teamwork

Though various options are available on the internet with a wide range of specifications, including duration, level, type, etc., picking the best courses for you can be overwhelming among many course providers. To ease this process, we have analyzed courses offered across different platforms.

Here are the top picks among the best deep-learning courses at Careervira -

• Deep Learning Specialization by Coursera: This program helps understand Deep Learning fundamentals and how to build neural networks and machine learning projects. It also covers topics like Convolutional networks, RNNs, LSTM, Adam, Dropout, and more. Learners get hands-on experience by working on case studies related to healthcare, autonomous driving, and natural language processing.
   
• Deep Learning Course with TensorFlow Certification Course by Edureka: This course aims to help learners master popular algorithms like CNN, RCNN, RNN, LSTM, and RBM using Python's latest TensorFlow 2.0 package. It is well-suited for professionals and beginners. Enrolled learners work on real-time projects like Emotion and Gender Detection, Auto Image Captioning using CNN and LSTM, and other relevant concepts.
   
• Professional Certificate in Deep Learning by edX: This program is offered by edX in collaboration with IBM. It helps learners understand concepts and applications in Deep Learning, including various Neural Networks for supervised and unsupervised learning. It also teaches how to apply the skills by building models and algorithms using libraries like Keras, PyTorch, and TensorFlow.
   
• Deep Learning Nanodegree Program by Udacity: Learners are taught how to build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation, etc. To enroll, you must have intermediate-level Python programming knowledge, experience with NumPy and pandas, and basic math knowledge, including algebra and some calculus.

Here are the top picks from the Neural network courses available at Careervira -

• Neural Networks and Deep Learning by Coursera: This course is specifically useful for beginners who want to learn essential Neural network concepts and key parameters in neural network architecture. It helps learners gain skills like backpropagation, python programming, artificial neural network, neural network architecture, etc.
   
• Deep Neural Networks with PyTorch by Coursera: Coursera offers it in collaboration with IBM experts. Learners are taught to master the basic principles of the machine and deep learning, including supervised and unsupervised learning. They also learn to capitalize on Python libraries for Deep Learning applications and build Deep Neural Networks using PyTorch.
   
• Neural Networks 1 by QuickStart: This course covers basic neural network architectures and learning algorithms for pattern recognition, image processing, and computer vision applications. Also, it teaches how to use popular Deep Learning libraries such as Keras, PyTorch, and TensorFlow for real industry problems.
   
• Literacy Essentials: Core Concepts Convolutional Neural Network by Pluralsight: By completing this course, learners gain the skills and knowledge required to understand how CNNs (Convolutional Neural Networks) work so they can start exploring more advanced Convolutional Neural Networks needed to add understanding of images to applications.