ANN Vs CNN Vs RNN - Exploring the Neural Networks in AI

May 6, 2022

What are the three important types of neural networks in AI that are regularly used and are mostly talked about? Let’s scroll more on how they operate, and how they are utilized.

The three most important neural networks are:

ANN (Artificial Neural Network)

CNN (Convolutional Neural Network)

RNN (Recurrent Neural Network)

First, let's start with a quick explanation of what neural networks in AI are.

What is a Neural Network in AI?

A neural network is a network or circuit made up of biological neurons, or, in a more contemporary meaning, an artificial neural network made up of artificial neurons or nodes. Thus, a neural network can be either a biological neural network (made up of biological neurons) or an artificial neural network in AI, made of nodes (used to solve Artificial Intelligence issues). Artificial neural networks mimic biological neuron connections as weights between nodes.

Neural Networks in AI can discover hidden patterns and correlations in raw data using algorithms, cluster and categorize them, and learn to improve over time.

However, just as different tasks need different understandings, neural networks are no exception. While a certain type of neural network might outperform for a specific type of problem, it may even underperform if applied to some other type of problem.

That is why the classification of neural networks is done, to optimize the results by using the right neural network for different use cases.

ANN vs CNN vs RNN-

There are hundreds of neural networks available to handle issues throughout many domains. In this section, we'll go through the classification of neural networks as ANN vs CNN vs RNN.

ANN Artificial Neural Network-

ANN learning has been effectively used to learn real-valued, discrete-valued, and vector-valued functions containing challenges such as analyzing visual scenes, voice recognition, and learning robot control techniques.

ANNs send data in one direction, passing it through multiple input nodes until it reaches the output node. Due to this, ANNs are also known as Feed-Forward networks. Hidden node layers may or may not exist in the network, making its operation more understandable and making ANNs one of the most basic neural network versions.

In ANNs, a problem might have numerous instances, each of which is represented by a set of attribute-value pairs. ANNs used to solve issues with a target function output can be discrete, real, or a vector of many real or discrete-valued properties. ANN learning methods do not have an issue with noise in the training data. There may be faults in the training samples, but they will have no effect on the final result. It is commonly utilized in situations when a quick assessment of the learned target function is necessary. The total number of weights in the network, the total number of training instances evaluated, and the settings of different learning algorithm parameters can all contribute to extended training durations for ANNs.

What should ANNs be used for?

The ANN is employed in technology that focuses on difficult issue solving, such as pattern recognition challenges.

Here are several examples:

For business intelligence, predictive analysis is used.

A speech-to-text transcription program that converts spoken words into text.

Recognition of handwriting and facial expressions.

Email spam detection.

Forecasting the weather.

Limitations of ANNs-

ANNs are capable of working only with numerical data. Before being brought to ANN, problems must be transformed into numerical values.

Experience and trial and error are used to create the ideal network structure as the structure of artificial neural networks are determined by no explicit rule.

The trust in ANN is low as when ANN provides a probing answer, it does not explain why or how it was chosen.

CNN (Convolutional Neural Network)

A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning system that can take an input picture, assign relevance (learnable weights and biases) to different aspects in the image, and distinguish between them. Or in other words, the CNN's job is to compress the pictures into a format that is easier to manage while retaining key components for generating a good forecast

This neural network computational model employs a multilayer perceptron variant and includes one or more convolutional layers that can be linked or pooled altogether. These convolutional layers provide feature maps that capture a portion of an image, which are then split down into rectangles and routed to nonlinear processing.

The first ConvLayer is in charge of collecting low-level details like edges, color, gradient direction, and so on. The design changes to the High-Level properties as well with the addition of layers, giving us a network that understands the photographs in the dataset in the same manner, as we do.

A CNN requires substantially less pre-processing than other classification algorithms. Whereas simple approaches require hand-engineering of filters,

With adequate training, CNNs can learn these characteristics.

What should CNNs be used for?

The most productive use for CNNs is image classification, for eg:- Labeling hand-written letters and digits or identifying satellite images that contain roads.

Limitations of CNNs

CNNs do not encode the position and orientation of objects, therefore if the pictures have a degree of tilt or rotation, CNNs have a hard time categorizing them.

Inability to be spatially invariant with respect to the supplied data.

Coordinate frames, which constitute an essential component of human vision, are not present in CNNs.

A ConvNet requires a large dataset to process and train the neural network.

RNN(Recurrent Neural Network)

RNNs make use of sequential data, such as time-stamped data from a sensor device or a spoken speech made up of a series of words. Unlike standard neural networks, a recurrent neural network's inputs are not independent of one another, and each element's output is reliant on the computations of the elements before it, the output from the previous phase is supplied into the current stage as input.

RNNs have a "memory" that stores all information about the calculations. Because it delivers the same result by doing the same job on all inputs or hidden layers, it uses the same parameters for each input. Unlike other neural networks, the complexity of the parameters is reduced.

RNNs are utilized in applications such as forecasting and time series analysis. With recurrent neural networks, even convolutional layers are used to extend the effective pixel neighborhood.

What should RNNs be used for?

RNN can produce pretty exact predictions since it has internal memory. Furthermore, it may be used to solve problems with sequential data.

In light of this, RNN applications include:

Prediction issues Automated translation

Speech recognition

Analysis of public sentiment

Forecasting stock prices

Text generation and language modeling

Limitations of RNNs

The problem of disappearing or exploding gradient RNNs can't be stacked.
RNNs are recurrent, which means that training them will take a long period.

When compared to feedforward networks, the overall training pace of RNN is rather slow.

It's more difficult to process longer sequences.

The Crux

So, what are you working on?

If you're just getting started with Machine Learning, it's critical to understand and identify the problem you're attempting to address.

Remember:

Artificial Neural Networks (ANNs) are useful for resolving complicated issues.

CNNs (Convolutional Neural Networks) are the most effective way to solve computer vision issues.

RNNs (Recurrent Neural Networks) are capable of processing natural language.

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November 4, 2024

A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.
CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels.

Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

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Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

This is a decorative image for: Constructing 3D objects through Deep Learning

October 18, 2022

Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

Highly calibrated cameras that take a photograph of the image from various angles.
Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website.

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Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

This is a decorative image for: Comprehensive Guide to Deep Q-Learning for Data Science Enthusiasts

October 18, 2022

A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

Reference Links

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

October 13, 2022

GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
GAUDI also removes GANs pathologies like mode collapse and improved GAN.
GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
The scenes are thus synthesized by interpolation within the hidden space.
The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

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Reference Links

https://www.researchgate.net/publication/362323995_GAUDI_A_Neural_Architect_for_Immersive_3D_Scene_Generation

https://www.technology.org/2022/07/31/gaudi-a-neural-architect-for-immersive-3d-scene-generation/

https://www.patentlyapple.com/2022/08/apple-has-unveiled-gaudi-a-neural-architect-for-immersive-3d-scene-generation.html