Deep Learning – Introduction to Recurrent Neural Networks

Recurrent Neural Networks: The main use of RNNs is when using Google or Facebook; these interfaces can predict the next word that you are about to type. RNNs have loops to allow information to persist. This reduces the complexity of parameters, unlike other neural networks. These neural nets are considered to be fairly good for modeling sequence data. Recurrent neural networks are a linear architectural variant of recursive networks. They have a memory,” so they differ from other neural networks. This memory remembers all the information about what was calculated in the previous state. It uses the same parameters for each input as it performs the same task on all the inputs or hidden layers to produce the output.

This post is a high-level overview for creating a basic understanding. Don’t expect too much if you are a PhD or master’s degree student. We will only focus on the intuition behind RNNs instead. You will get a little comfort from starting to dig deeper into RNNs.

Artificial Neural Networks – What Is It

In 1943, McCulloch and Pitts designed the first neural network. Artificial neural networks were modelled on a simplified version of human brain neurons.

As per wiki “Recurrent neural network is a class of artificial neural network where connections between nodes form a directed graph along a sequence.” This allows it to exhibit temporal dynamic behaviour for a time sequence.

There are several kinds of Neural Networks in deep learning.

• Multi-Layer Perceptron
• Radial Basis Network
• Recurrent Neural Networks
• Generative Adversarial Networks
• Convolutional Neural Networks.

As per AILabPage, Artificial neural networks (ANNs) are “Complex computer code written with several simple, highly interconnected processing elements that is inspired by human biological brain structure for simulating human brain working and processing data (Information) models”.

It is important to note that artificial neural networks are way different from computer programs, so please don’t get the wrong perception from the above definition. Neural networks consist of input and output layers and at least one hidden layer.

Training neural networks can be hard, complex, and time-consuming. The reasons are simply known to data scientists. One of the major reasons for this hardship is weight. In Neural networks, weights are highly interdependent with hidden layers. The three main steps to training neural networks are

1. Forward pass and makes a prediction.
2. Compare prediction to the ground truth using a loss function.
3. Error value to do backpropagation

The algorithm to train ANNs depends on two basic concepts: first, reducing the sum squared error to an acceptable value, and second, having reliable data to train the network under supervision.

Recurrent Neural Networks- Introduction

Recurrent neural networks are not too old neural networks, they were developed in the 1980s.

• RNNs takes input as time series and provide an output as time series,
• They have at least one connection cycle.

One of the biggest uniquenesses RNNs have is their “UAP” (universal approximation property), so they can approximate virtually any dynamical system. This unique property forces us to say that recurrent neural networks have something magical about them.

There is a strong perception of the recurrent neural network training part. The training is assumed to be super complex, difficult, expensive, and time-consuming. As a matter of fact, after a few hands-on experiences in our lab, our response is just the opposite. So common wisdom is completely opposite from reality. The robustness and scalability of RNNs are super exciting compared to traditional neural networks and even convolutional neural networks.

Recurrent Neural Networks are way more special as compared to other neural networks. Non-RNN APIs have too many constraints and limitations (sometimes RNNs also do the same, though). Non-RNN API take

• Input – Fixed size vector: For example an “image” or a “character”
• Output – Fixed size vector: Probability matrix
• Size of Neuron – Fixed number of layers / computational steps

We need to answer “What kind of problems can be solved with “Recurrent Neural Networks”? before we go any deeper in this.

Solving Complex Problems with Sequential Data

Recurrent Neural Networks (RNNs) are well-suited for solving a wide range of problems that involve sequential or time-series data. Some of the key problems that can be effectively addressed using RNNs include:

1. Natural Language Processing (NLP): RNNs are commonly used in tasks such as language translation, sentiment analysis, text generation, and speech recognition. Their ability to capture temporal dependencies in language sequences makes them valuable for understanding and generating human language.
2. Time Series Analysis: RNNs are ideal for time series prediction and forecasting, enabling accurate predictions based on historical data. They can be applied to financial forecasting, stock market analysis, weather prediction, and other time-dependent data analysis tasks.
3. Speech Recognition: RNNs excel in converting spoken language into written text. They can process audio data over time to recognize speech patterns and convert them into textual representations.
4. Music Generation: RNNs can be used in music composition and generation tasks. By learning patterns and structures from existing music, RNNs can create new musical pieces that follow similar styles and harmonies.
5. Video Analysis: RNNs can be applied to video analysis tasks, such as action recognition, object tracking, and activity forecasting. They can process sequential frames of a video to understand temporal patterns and movements.
6. Natural Language Generation: RNNs can generate coherent and contextually relevant text based on given prompts or input, making them useful in chatbots, automatic summarization, and creative writing applications.
7. Sentiment Analysis: RNNs can determine the sentiment of a given piece of text, classifying it as positive, negative, or neutral, which is valuable for sentiment analysis in customer feedback and social media data.
8. Gesture Recognition: RNNs can recognize gestures from motion capture data, making them applicable in virtual reality and human-computer interaction systems.

These are just a few examples of the diverse problems that RNNs can effectively solve. Their ability to handle sequential data and capture temporal dependencies makes them a powerful tool for a wide range of real-world applications across various industries.

Real life examples – Recurrent Neural Networks

When we deal with RNNs, they show excellent and dynamic abilities to deal with various input and output types. Before we go deeper, let’s look at some real-life examples.

• Varying Inputs and Fixed Outputs: Speech, Text Recognition, and Sentiment Classification: In today’s time, this can be the biggest relief for a bomb like social media to kick out negative comments. People who like to give only negative comments for anything and everything rather than helping have one motive: to pull him/her down) someone’s efforts. Classifying tweets and Facebook comments into positive and negative sentiments becomes easy here. Inputs with varying lengths, while the output is of a fixed length.
• Fixed Inputs and Varying Outputs: Image Recognition (Captioning): This is to describe the content in an image. Images as a single input, but captions can be a series or sequence of words as an output. Kids riding a bike, children playing in the park, young girls playing football, two girls dancing, etc.
• Varying Inputs and Varying Outputs: Machine Translation and Language Translation Translating one language to another can be a tedious task for humans. It is done word by word from the dictionary, but thanks to this amazing tool from Google’s online translation to full text, This tool is so powerful because it takes care of sentiments in each language, their length, and their meanings in context. This is the case for varying inputs as well as varying outputs.

As evident from the cases discussed above, Recurrent Neural Networks (RNNs) excel at mapping inputs to outputs of diverse types and lengths, showcasing their versatility and broad applicability.

The underlying foundation of RNNs lies in their ability to handle sequential data, making them inherently suitable for tasks involving time series, natural language processing, audio analysis, and more. The generalized nature of RNNs allows them to adapt and learn from temporal dependencies in data, enabling them to tackle a wide range of problems and deliver meaningful insights in various domains.

Their capacity to capture context and temporal relationships in sequential data makes RNNs a valuable tool for addressing real-world challenges, where the length and complexity of input-output mappings may vary considerably. By leveraging this inherent flexibility, developers and researchers can employ RNNs as a fundamental building block for constructing innovative and sophisticated models tailored to their specific data-driven needs.

Recurrent Neural Networks & Sequence Data

As we know by now, RNNs are considered to be fairly good for modeling sequence data. Let’s understand sequential data a bit. While playing cricket, we predict and run in the direction where the ball moves. This means recurrent networks take current input examples they see and also what they have perceived previously in time. This happens without any guessing or calculation because our brain is programmed so well that we don’t even realize why we run in a ball’s direction.

If we look at the recording of ball movement later, we will have enough data to understand and match our action. So this is a sequence—a particular order in which one thing follows another. With this information, we can now see that the ball is moving to the right. Sequence data can be obtained from

• Audio files: This is considered a natural sequence. Audio file clips can be broken down in the audio spectrogram and fed into RNNs.
• Text file: Text is another form of sequence; text data can be broken into characters or words (remember search engines guessing your next word or character).

Can we comfortably say that RNNs are good at processing sequence data for predictions based on our examples above? RNNs are gaining more attraction and popularity for one core reason: they allow us to operate over sequences of vectors for input and output, not just fixed-size vectors. On the downside, RNNs suffer from short-term memory.

Use cases – Recurrent Neural Networks

Let’s understand some of the use cases for recurrent neural networks. There are numerous exciting applications that got a lot easier, more advanced, and more fun-filled because of RNNs. Some of them are listed below.

• Music synthesis
• Speech, text recognition & sentiment classification
• Image recognition (captioning)
• Machine Translation – Language translation
• Chatbots & NLP
• Stock predictions

To comprehend the intricacies of constructing and training Recurrent Neural Networks (RNNs), including widely utilized variations like Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) networks, it is essential to delve into the fundamental principles and underlying mechanisms of these sophisticated architectures.

Mastering the art of RNNs entails grasping the concept of sequential data processing, understanding the role of recurrent connections in retaining temporal information, and exploring the challenges posed by vanishing or exploding gradients. By gaining proficiency in these techniques, developers and researchers can harness the power of RNNs to address a myriad of real-world problems, ranging from natural language processing and speech recognition to time series analysis and beyond.

There are a lot of free and paid courses available on the internet. At AILabPage, we also conduct hands-on classroom training in our labs to train deep learning enthusiasts. These courses can help you solve natural language problems, including text synthesis. Ultimately, you will have the opportunity to build a deep learning project with cutting-edge, industry-relevant content.

RNN models have demonstrated exceptional performance in handling temporal data. They encompass various variations, such as LSTMs (long short-term memory), GRUs (gated recurrent units), and Bidirectional RNNs. These sequence algorithms have significantly simplified the process of constructing models for natural language, audio files, and other types of sequential data, making them more accessible and effective in various applications.

Vanishing and Exploding Gradient Problem

The deep neural network has a major issue around gradients as it is very unstable. Due to its unstable nature, it tends to either explode or vanish from earlier layers quickly. The vanishing gradient problem emerged in the 1990s as a major obstacle to RNNs’ performance. In this problem, adjusting weights to decrease errors and the “synch problem” lead the network to cease to learn at the very early stage itself.

The problem encountered by Recurrent Neural Networks (RNNs) had a significant impact on their popularity and usability. This issue arises from the vanishing or exploding gradients phenomenon, which occurs when the RNN attempts to retain information from previous time steps. The nature of RNNs to maintain a memory of past values can lead to confusion, causing the current values to either skyrocket or plummet uncontrollably, overpowering the learning algorithm.

As a result, an undesirable situation of indefinite loops arises, disrupting the network’s ability to make further progress and effectively bringing the entire learning process to a standstill. This challenge posed significant obstacles to the practical application and widespread adoption of RNNs, prompting researchers and developers to seek alternative architectures and techniques, such as LSTMs (Long Short-Term Memory) and GRUs (Gated Recurrent Units), which have proven more effective in addressing the vanishing and exploding gradient issues while preserving the temporal dependencies in sequential data.

For example, neurons might get stuck in a loop where they keep multiplying the previous number by a new number, which can go to infinity if all numbers are more than one or get stuck at zero if any number is zero. And it depends on how much time you have. For us at AILabPage, we say machine learning is a crystal-clear and simple task. It is not only for PhD aspirants; it’s for you, us, and everyone.

Not Covered here

Topics we have not covered in this post but are extremely critical and important to understand to get a little more strong hands-on RNNs as below.

• Sequential Memory
• Backpropagation in a Recurrent Neural Network(BPTT)
• LSTM’s and GRU’s

Points to Note:

All credits if any remain on the original contributor only. We have covered all basics around Recurrent Neural Networks. RNNs are all about modelling units in sequence. The perfect support for Natural Language Processing – NLP tasks. Though often such tasks struggle to find the best companion between CNN’s and RNNs’ algorithms to look for information.

Books + Other readings Referred

• Research through open internet, news portals, white papers and imparted knowledge via live conferences & lectures.
• Lab and hands-on experience of  @AILabPage (Self-taught learners group) members.
• This useful pdf on NLP parsing with Recursive NN.
• Amazing information in this pdf as well.

Feedback & Further Question

Do you have any questions about Deep Learning or Machine Learning? Leave a comment or ask your question via email. Will try my best to answer it.

Conclusion –  I particularly think that getting to know the types of machine learning algorithms actually helps to see a somewhat clear picture. The answer to the question “What machine learning algorithm should I use?” is always “It depends.” It depends on the size, quality, and nature of the data. Also, what is the objective/motive of data torturing? As more we torture data more useful information comes out. It depends on how the math of the algorithm was translated into instructions for the computer you are using.

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