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What is Deep Learning? Simple Explained

Deep learning is a type of machine learning in which a model learns to perform classification tasks directly from images, text, or sound. Deep learning is usually implemented using a neural network architecture. The term “deep” refers to the number of layers in the network – the more layers, the deeper the network. Traditional neural networks contain only 2 or 3 layers, while deep networks can have hundreds.

Self-driving cars, Automation, Robots taking over jobs. These are just a few of the topics that the media covers when discussing artificial intelligence (AI). Of course, the story is far more complex, and the burgeoning fields of AI, machine learning, and deep learning can have a profound impact on your business, your career, and your life on a daily basis.

Deep Learning is a subfield of machine learning that is concerned with algorithms inspired by the brain’s structure and functions known as artificial neural networks

A computer model can be taught using Deep Learning to run classification actions using pictures, texts or sounds as input?

UCLA researchers built an advanced microscope that yields a high-dimensional data set used to train a deep learning network to identify cancer cells in tissue samples.

Three technology enablers make this degree of accuracy possible:

  1. Easy access to massive sets of labelled data: Data sets such as ImageNet and PASCAL VoC are freely available and are useful for training on many different types of objects.
  2. Increased computing power: High-performance GPUs accelerate the training of the massive amounts of data needed for deep learning, reducing training time from weeks to hours.
  3. Pretrained models built by experts: Models such as AlexNet can be retrained to perform new recognition tasks using a technique called transfer learning. While AlexNet was trained on 1.3 million high-resolution images to recognize 1000 different objects, accurate transfer learning can be achieved with much smaller datasets.

What Makes Deep Learning State of the Art

Just a few examples of deep learning at work are:

  • A self-driving vehicle slows down as it approaches a
    pedestrian crosswalk.
  • An ATM rejects a counterfeit banknote.
  • A smartphone app gives an instant translation of a foreign
    street sign.

Deep learning is especially well-suited to identification applications such as face recognition, text translation, voice recognition, and advanced driver assistance systems, including, lane classification and traffic sign recognition.

Deep Neural Network

Deep Neural Network

Definition of Deep Neural Network

A deep neural network combines multiple nonlinear processing layers, using simple elements operating in parallel and inspired by biological nervous systems. It consists of an input layer, several hidden layers, and an output layer. The layers are interconnected via nodes, or neurons, with each hidden layer using the output of the previous layer as its input.

What is a Deep Neural Network?

A deep neural network (DNN) is an artificial neural network (ANN) with multiple layers between the input and output layers. There are different types of neural networks but they always consist of the same components: neurons, synapses, weights, biases, and functions.

How do deep neural networks work?

Neural networks are layers of nodes, much like the human brain is made up of neurons. Nodes within individual layers are connected to adjacent layers. The network is said to be deeper based on the number of layers it has. A single neuron in the human brain receives thousands of signals from other neurons.

What is the difference between a CNN and deep neural network?

The main difference between a CNN and an RNN is the ability to process temporal information – data that comes in sequences, such as a sentence. Recurrent neural networks are designed for this very purpose, while convolutional neural networks are incapable of effectively interpreting temporal information.

Why use deep neural networks?

Deep neural networks are key in helping computers have the resources and space they need to answer complex questions and solve larger problems. Normal neural networks may only have a few hidden layers; deep neural networks may have hundreds of hidden layers to help solve a problem and create an output

Deep Learning Process

Deep Learning Process

Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input. The process is articulated in 5 steps:

  • Step 1Understand the Problem.
  • Step 2: Identify Data
  • Step 3Select Deep Learning ALgorithms
  • Step 4Training the Model
  • Step 5: Test the Model

AI Vs Machine Learning VS Deep Learning

Artificial Intelligence (AI) is a popular branch of computer science that concerns building “intelligent” smart machines capable of performing intelligent tasks. With the rapid advancement in deep learning and machine learning, the tech industry is transforming radically.

AI VS Machine Learning VS Deep Learning 1

Artificial Intelligence is the concept of creating smart intelligent machines. Machine Learning is a subset of artificial intelligence that helps you build AI-driven applications. Deep Learning is a subset of machine learning that uses vast volumes of data and complex algorithms to train a model.

Artificial Intelligence

  • Artificial Intelligence originated around the 1950s.
  • AI represents simulated intelligence in machines.
  • AI powers Data Science operations.
  • The aim is to build machines that are capable of thinking like humans.

Machine Learning

  • Machine Learning originated around the 1960s.
  • Machine learning is a technique for getting machines to make decisions without being programmed.
  • Machine Learning is a subset of AI & Data Science.
  • The aim is to make machines learn through data so that they can solve problems.

Deep Learning

  • Deep learning originated around the 1970s.
  • Deep Learning is the process of using artificial neural networks to solve complex problems.
  • Deep Learning is a subset of MachineLearning AI & Data Science.
  • The aim is to build Neural Networks able to automatically discover patterns for feature detection.

To summarize, with rule-based AI and machine learning, a data scientist has primary responsibility for deciding which rules and data set features will be included in models, which drives how those models operate. With deep learning, data scientists submit raw data into an algorithm. The system analyzes that data and, based on what it knows and what it can infer from the new data you’ve presented, it makes a prediction.

Application of Deep Learning

Deep Learning algorithms are reaching unprecedented levels of precision, to such an extent that they surpass humans in image classification and are able to beat the best GO player in the world. Deep Learning is the technology that allows driverless cars to recognize a stop sign or distinguish a pedestrian from a street lamp. It is the key factor of voice control in smartphones, tablets, TVs and hands-free speakers.

Application of Deep Learning

Top application of Deep Learning:

  • Self Driving Cars.
  • Portfolio Management and Prediction of stock Price Movements.
  • News Aggregation and Fraud News Detection.
  • Natural Language Processing.
  • Virtual Assistants.
  • Entertainment.
  • Visual Recognition.
  • Fraud Detection.
  • Healthcare.


Machine learning algorithms learn from data to find hidden relations, to make predictions, to interact with the world, …
A machine learning algorithm is as good as its input data
Good model + Bad data = Bad Results
Deep learning is making significant breakthroughs in speech recognition, language processing, computer vision, control systems, …
If you are not using or considering using Deep Learning to understand or solve vision problems, you almost certainly should be.