The World of Advanced Machine Learning Algorithms
Mastering the Complexity
It’s the right time discover the power of modern AI algorithms, and delve deeper into neural networks, Convolutional Neural Networks (CNNs), Recurrent Neural networks (RNNs), and Generative Adversarial Networks (GANs) while we walk you through the complex steps by which machine algorithms learn, think, and create.
The objective approach has proven to be very useful when selecting the right algorithm for a particular purpose.
Neural Networks
At the outset, to explain simplistically – Neural networks are algorithms that mimic the structure and function of the human brain.
They are trainable in complex patterns and make correct decisions.
Here’s what the experts have alluded to on the subject!
An Incisive book titled – “Deep Learning” by I. Goodfellow, Y. Bengio, and A. Courville (2016) is a comprehensive work that provides an all-inclusive analysis of neural network formation.
By far, Deep Learning provides a solid foundation for researchers who are interested in exploring current and future research directions.
The book includes network design, training, assessment, and fine-tuning.
To affirm it, the authors offer a solid theoretical foundation and practical suggestions based on their research.
The explanations provide a clear understanding of the mathematical concepts behind deep learning, making it easier for AI researchers from various subfields to grasp them.
The extensive bibliography serves as a valuable resource for obtaining additional information.
Indeed, Deep Learning is an excellent resource for AI researchers who are interested in neural networks because it provides a thorough and easy-to-understand explanation of deep learning and related technologies.
Deep Learning: CNNs
CNNs belong to a class of deep neural networks applied mostly to the analysis of visual imagery.
In terms of image and video recognition, they have been known contributors.
Chitkara University Institute of Engineering and Technology in Punjab, India, states that an empirical study on deep learning models by Monika Sethi, Sachin Ahuja, and Vinay Kukreja indicates that DL models have proven to be effective in addressing various challenging problems.
More specifically, computer vision, text analysis, speech recognition, and categorization –
Models of this nature require ample space and meticulous attention to detail.
The Convolutional Neural Network (CNN) is a highly popular neural network that excels at automated feature extraction, distinguishing it from traditional machine learning algorithms (CMLA).
This work presents a method for developing device architectures that streamlines the process of creating versions, generating alternatives, and evaluating them.
The aim is to efficiently explore a wide range of design options and minimize the effort required to create architectures.
CNN’s main operations consist of convolution, pooling, flattening, and full input-output connection.
By far, the study utilizes CIFAR 10, which is a dataset consisting of 60,000 color images that cover 10 different categories.
And the categories include vehicles, trucks, frogs, horses, cats, aircraft, ships, and deer.
Improving the CNN model’s performance can be achieved by implementing a more intricate network design, increasing the number of epochs, or applying data augmentation techniques.
This study provides an explanation and comparison of both basic and sophisticated CNN models.
The CIFAR 10 dataset is utilized to evaluate the accuracy of these models.
Recurrent Neural Networks (RNNs)
RNNs are particularly useful for predicting sequences.
They are looped networks that are information-permanent, so they are the best structure for such tasks as language modelling.
It was fascinating to see how Hopfield networks were revolutionary.
In 1990, Jeffrey Elman presented the Elman Network, the first recurrent network that successfully used backpropagation for training (Elman, 1990).
Back then, Elman was a cognitive scientist at the University of California, San Diego, and he was a part of the research team that wrote the famous PDP book.
Elman had a significant impact on the field of cognitive science with the publication of “Finding Structure in Time” in 1990.
The difficulty of adding “time” or “sequences” to neural networks was Elman’s primary concern.
Generative Adversarial Networks (GANs)
Interestingly, the generator and discriminator are two neural networks in GANs that are trained together competitively.
GAN is a state-of-the-art deep learning generative modelling technique that leverages convolutional neural networks.
Generative modelling is capable of automatically identifying patterns within incoming data and producing new samples that accurately mirror the original dataset.
The article “A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications” from IIT, Kanpur, provides valuable insights into the popularity of GANs as a research area.
GANs have been the subject of extensive research since 2014, with a variety of methods proposed.
There is a lack of research that delves into the origins and interconnections of different GAN variations.
This article provides a comprehensive review of GAN approaches, covering algorithmic, theoretical, and practical aspects.
First, we will delve into the motives, mathematical representations, and structure of most GAN algorithms.
Additionally, researchers have combined GANs with other machine learning algorithms to enhance semi-supervised, transfer, and reinforcement learning.
This study examines the similarities and differences of various GAN approaches.
Next, the theory of GAN is explored.
Furthermore, image processing, computer vision, natural language processing, music, voice, audio, medicine, and data science all use GANs in a variety of ways.
Lastly, we identify the future challenges in GAN research.
Understanding the functioning of GANs
There are three categories that GANs can be classified into:
- Innovative: A probabilistic model generates data.
- Challenging: Training a model through adversarial training.
- Networks: Deep neural networks can be utilized in AI training techniques.
Choosing the Right Algorithm
Factors to Consider
Choosing the right algorithm is a function of the problem, the quality of the data, its complexity, and the available computational resources.
- Categorize your problem.
Well, that’s going to put a damper on your algorithm options.
Supervised learning relies on the use of labelled goal values.
Predominantly, unsupervised learning is all about discovering patterns and structures in data without any specific objectives in mind.
Reinforcement learning can be a bit challenging due to the need for active participation in the environment and receiving timely feedback.
These three learning methods reign supreme:
- The data output might expose some problems with classification or regression.
- Regression is used for numerical output,
- While classification is used for categorical output.
- Get familiar with the data.
In order to gain a better understanding of your current predicament, kindly provide responses to the following inquiries:
- What kind of data are you referring to?
- Wow, you’ve got quite a few features, huh?
- So, are you more of a number’s person, or
- Do you prefer working with categories?
- Is your data linear?
- Are you looking for binary or multi-class categorical targets?
So, we’ve got a bunch of outliers, huh?
Check out these interesting points:
For situations where there is limited data and a large number of attributes, it is recommended to use generalization techniques with high bias and low variance.
Some examples of these techniques include Linear Regression, Naïve Bayes, linear SVM, and logistic regression.
Support Vector Machines are great at handling problems with a high number of features.
When it comes to dealing with lots of data and a limited number of features, we opt for algorithms that have a low bias and high variance.
This helps us learn efficiently and avoid underfitting. Some examples of such algorithms include KNN, Decision Tree, Random Forest, Kernel SVM, and neural nets.
In addition, PCA has a knack for always reducing features.
Machine learning methods may be more effective since neural networks struggle with limited data points.
If your data happens to have a bunch of outliers that you actually care about, then linear regression, logistic regression, and other methods that are sensitive to outliers might not be the best fit for you.
Random Forest conveniently overlooks outliers.
Check out the details right here.
Certain algorithms, like linear regression, logistic regression, and linear SVM, tend to favour linear connections.
Random Forest, Kernel SVM, Gradient Boosting, and Neural Nets excel at handling intricate data structures.
If you’re looking for binary targets, logistic regression or SVM might be worth considering.
For those multi-class objectives, a more intricate model such as Random Forest or Gradient
Boosting might be necessary.
An algorithm could potentially be multiclass logistic regression.
- What are your expectations?
The algorithm you choose will depend on what you’re trying to achieve.
Is the model aligned with corporate objectives?
Feel free to adjust the limits for accuracy, speed, recall, precision, or memory footprint.
Self-driving cars require rapid predictions.
Consider the speeds of different algorithms and make your selection.
Opt for simpler algorithms such as Naïve Bayes, Linear, and Logistic regression to make training easier and improve the overall results.
Time constraints, the need for straightforward data, and the importance of clear interpretation can all contribute to this issue.
In addition, approximation methods help prevent overfitting and have good generalization capabilities.
Algorithm parameters are also important to consider.
The time it takes to train a model skyrocket as the number of parameters increases, as you need to diligently search for the perfect pattern.
If you’re pressed for time, give this a whirl.
When it comes to medical diagnosis, prediction and training time take a backseat.
For the highest level of accuracy, you might want to consider using deep learning, neural nets, or complex models like XGBoost.
For sure, the model you choose will depend on the key factors in your scenario.
Numbers aren’t always the be-all and end-all when it comes to making a decision.
Our interpretation influences the choice of model.
Models that are easy to understand allow for effective problem-solving.
Choosing the right strategy for your goal involves a well-known trade-off between accuracy and interpretability.
- Experiment with various models.
Begin with basic models and progress once you have a clear understanding of your goals and the effective algorithms.
Starting with the most straightforward approach in your preferred algorithm allows you to bypass neural networks if they meet my goals.
In order to evaluate the performance of each model class, it would be beneficial to experiment with various hyperparameter combinations.
However, since our primary focus is on filtering algorithms, let’s not overdo this process.
Evaluation metrics provide a variety of ways to assess the performance of models.
Find the perfect algorithm for your task by putting all your algorithms to the test with some basic hyper-parameters.
Let’s compare and fine-tune the hyperparameters.
Your machine learning pipeline will assess your final algorithms based on specific criteria using your dataset.
Let’s supercharge those algorithms with some grid search optimization!
Machine Learning is a dynamic process, allowing you the flexibility to fine-tune features as needed.
Feature engineering has the power to shake up the top algorithm.
In summary…
Choosing the right algorithm for a specific task is purely based on objective criteria.
Practice helps, but we hope our explanation has given you a better understanding of how to approach your strategy rather than relying on trial and error.
Several subjects deserve their own article, such as neural networks and reinforcement learning algorithms.
Keep coding, everyone!
That’s not all! There’s more in the next article. Stay tuned!Â
