Deep Learning Tutorial for Beginners

Every morning I start my day by checking the weather report. I simply ask Siri to tell me the weather report of the day. Our lives are surrounded by technology, but have you ever thought about how Google Assistant or Siri can understand exactly what you need?

It is through a combination of AI and deep learning. Just like the human brain comprises neurons, deep learning networks are made up of artificial neurons.

Let us explore the working, structure, and applications of deep learning networks in this deep learning tutorial that I have curated for beginners like you.

Introduction to Deep Learning

Deep learning is a subset of machine learning (ML) that mimics the human brain's neuron network. It uses artificial neural networks to process data just like the human brain.

Deep learning tutorial models can recognize complex details in texts, pictures, sounds, and other forms of data. It uses this information to produce accurate predictions and insights.

Deep learning can learn and improve based on user behavior over time. I have used many deep learning methods to automate tasks that generally require human intelligence, like transcribing a sound to text or describing an image.

Components of Deep Learning Network

To understand the basics of deep learning, you must understand the components of a neural network. The parts of a deep learning network are the following:

1. Input layer: Nodes receive data in the neural network, serving as the initial entry point for information processing, essential for initiating the network's computations.

2. Hidden layer: Analyzes data at different levels, examining features like color, shape, size, and texture to identify patterns and classify information effectively, enabling the network to learn intricate relationships within the data.

3. Output layer: Generates final data output, which can vary from binary outcomes (yes/no) to more nuanced responses, with the number of nodes reflecting the complexity of potential answers, thereby providing the ultimate decision or prediction based on the network's analysis.
   

What are Neural Networks?

Neural networks form the core of deep learning concepts. These networks form the functional units of a deep learning system. They are known to mimic the behavior of the human brain for solving complex data-driven problems. Input data is processed through several layers of artificial neurons stacked together to produce the desired output.

If you want to understand the workings of neural networks, enrolling in a deep learning online course can be beneficial.

Components of a Neural Network Architecture

Here are the components of an artificial neuron.

Let us understand the different parts of the deep learning diagram given above.

1. Input: These are the set of features fed into the model for the learning process. Let's say, you want to perform object detection of an image. You will start by providing an array of pixel values of the image.

2. Weight: Its main goal is to give priority to the features that contribute more towards the learning. It uses scalar multiplication between the input values and the weight matrix.

3. Transfer function: The purpose of this function is to combine multiple input values into one output value such that the application function is applicable. This is done by summation of all the input values to the transfer function.

4. Activation function: In the working of perceptrons, this introduces non-linearity, for considering varying input linearity. Without this function, the output would have been a linear combination of input values, and non-linearity would not be introduced into the network.

5. Bias: This is responsible for shifting the values produced by the activation function.
   

Types of Deep Learning Neural Networks

You possibly cannot have a holistic deep learning tutorial without learning about its types. Here are some of the different types of deep learning neural networks:

1. Feed-forward neural network

This artificial neural network ensures that the nodes do not form a cycle. All the perceptrons in this neural network are organized within the layers i.e., the input layer takes in the input and the output layer generates the output.

Every perceptron contained in a single layer is associated with every node in the subsequent layer. All the nodes in the network are connected, however, there are no connections between the nodes present in the same layer.

In this network, there are no back loops. The backpropagation algorithm is used for updating weight values, to minimize prediction error. Let’s say, you want to perform image recognition, this is the neural network that will be used.

Some other applications are as follows:

• Speech recognition
• Pattern recognition
• Data compression
• Handwriting recognition
• Sonar target recognition
  

2. Convolutional neural networks (CNN)

It is a multilayer neural network biologically inspired by the animal visual cortex. CNN has hidden convolutional layers forming the ConvNets.

As a deep learning network, early layers recognize the minute features of the image (such as borders, edges, shapes, textures, etc.) and the later layers recombine those features into higher-level input attributes. The deeper the network, the more sophisticated the pattern searching.

It is used for performing tasks such as image analysis, language processing, and various complex image classifications.

Some applications of CNN are:

• Feature learning
• Filtering
• Risk detection
• Classification
• Economic and business analysis
  

3. Recurrent neural network (RNN)

In a basic deep learning architecture, there is a fixed input size which acts as a blockage in cases where the input size varies. The RNN model works very well with a sequence of input data.

These networks have the power to remember what it has learned in the past and apply it for future predictions. Every time the hidden internal state of RNN reads the data sequence, it gets updated. This internal hidden state is fed back to the model. This neural network produces an output at every time stamp.

Some applications of RNN are:

• Robot control
• Machine translation
• Speech recognition
• Rhythm learning
• Time series anomaly detection
• Time series prediction
  

4. Restricted Boltzmann Machines

Restricted Boltzmann machines are a two-layered neural network, consisting of only the input and the hidden layers. Every node in the hidden layer of this network is connected to each node in a visible layer.

In traditional Boltzmann Machine networks, the nodes within the input and hidden layers are also connected. However, in a Restricted Boltzmann Machine, nodes within a layer aren't connected because of computational complexity.

Some applications of the Restricted Boltzmann Machine are:

• Feature learning
• Filtering
• Risk detection
• Classification
• Economic and business analysis
  

5. Autoencoder

This artificial neural network comprises three layers: input, hidden, and out layers. The input layer is first encoded into the hidden layer with a proper encoding function. Number of nodes in the input layer are much more than the number of nodes in the hidden layer.

The hidden layer comprises the compressed representation of the original input data. The input layer is reconstructed by the output layer using a decoder function.

The difference between the input and output layers is calculated using an error function during the training phase. The weights are also adjusted to minimize the error.

Some applications are:

• Clustering
• Classification
• Feature compression

Want to learn about the other types of neural networks in deep learning? Enroll in a deep learning tutorial for beginners to get an in-depth idea.

Applications of Deep Learning

Deep learning systems are made up of neural networks of two to three layers. A neural network attempts to model the behavior of the human brain by learning from large data sets.

Here are some uses of deep learning in today’s world:

Fraud detection

Fraud is a recurring problem in the digital world. According to a report by the Federal Trade Commission, 2.4 million fraud cases have been reported. To prevent fraud, many companies use deep learning to detect anomalies in user transactions.

Deep learning is deployed to collect data from various sources, such as credit card purchase patterns, device location, etc, for creating unique user profiles. This helps in identifying any fraudulent behavior.

Virtual assistance

The gap between machines and humans continues to blur as virtual assistance becomes a part of our everyday lives. These tools are driven by AI, but display a mix of machine learning, AI, and deep learning techniques to process commands.

Google’s Google Assistant and Apple’s Siri are two prominent examples of virtual assistants that can operate across speakers, laptops, TVs, and other devices.

Computer vision

The goal of deep learning systems is to mimic how the human mind detects patterns from digested information. It is the ideal way to train vision-based AI programs.

A deep learning system uses mathematical calculations for automatically processing various aspects of image data and gradually develops a combined understanding of the image.

Natural language processing

Using this technology, robots can read messages and derive meaning from them. However, the process can somewhat be oversimplified, not able to account for how words combine to change the intent or meaning behind a sentence.

With deep learning, natural language processors are able to identify more complex patterns in sentences to provide a more accurate interpretation. One of the best examples of this is Grammarly. It uses deep learning along with grammatical patterns and roles for identifying writing errors and gauging the tonality.

Self-driving cars

When driving, external factors are the key things to keep in mind. Even though we might be far from fully autonomous vehicles, deep learning plays a crucial role in making this technology come to action.

Let's say I am driving an autonomous vehicle. Once the vehicle knows where I want to go, it predicts the obstacles in the path and creates a safe path for me to reach my destination.

Apart from the applications discussed in this deep learning tutorial, there are several other uses of it.

Summing Up

Deep learning networks have a wide range of applications as you’ve come to know from this detailed deep learning tutorial. Artificial neural networks in deep learning systems sure can perform complex tasks with minimal assistance from the programmer.

The field of AI is growing at an exponential rate, however, there are not many qualified candidates to work in the field. Taking up a professional deep learning course can help you learn about the fundamentals of AI and this concept as a whole.

If you want to make a career in the field, check out the certified deep learning online course and similar other courses offered by upGrad.

Frequently Asked Questions

1. What is deep learning?

Deep learning, a subset of machine learning, uses multi-layered neural networks, for stimulating the complex reasoning power of the human brain.

2. Does deep learning need training?

The deep learning interface will not happen without training. Deep learning models are trained with large labeled data sets.

3. What is the purpose of deep learning?

There are several advantages of deep learning. It is used for performing complex tasks such as image recognition, speech recognition, feature extraction, etc. It can ingest and process unstructured data.

4. How does deep learning differ from traditional machine learning?

Machine learning models generally perform well with comparatively small data sets. Whereas, deep learning models need large data to understand and perform properly.

5. What are some popular deep learning frameworks?

Some popularly used deep learning frameworks are TensorFlow, PyTorch, DGL, PyTorch Geometric, etc.

6. What are the key components of a deep learning model?

The three main components of a DL model are:

• The input layer
• The hidden layer
• The output layer
  

7. What are some common challenges in deep learning?

One of the main disadvantages of deep learning is the requirement of large amounts of labeled training data to learn from effectively. The unavailability of proper data can be an issue. Another issue in these networks is the overfitting of input data.

8. What are some applications of deep learning?

Deep learning models are used in image processing, signal processing, computer vision, automated driving, etc.