How can you program artificial intelligence algorithms from scratch?

In my experience Python, famed for its simplicity and adaptability, is a top choice. It provides a solid foundation for creating large AI models with powerful libraries such as NumPy and TensorFlow. Its readability also encourages teamwork and is easy to learn, making it suitable for beginners. R, is popular for its statistical packages, for those who value statistical insights in AI applications. Languages like C++ or Java offer speed and efficiency, but are harder to master. Although algorithmic competence is essential, the programming language used has a significant impact on efficiency and maintainability, your decision determines the canvas for AI model building—choose wisely.

Creating an AI algorithm involves defining the task, choosing an appropriate technique (like machine learning or neural networks), gathering and preprocessing data, implementing the algorithm using suitable programming languages and frameworks, training it with the prepared data, evaluating its performance, and iterating to improve accuracy and efficiency. This process includes steps like problem definition, algorithm selection, data collection/preprocessing, coding the algorithm, training/evaluation, and continuous refinement through parameter adjustments, feedback incorporation, and dataset expansion.

To build an AI algorithm from scratch you need to: - Identify the problem and the desired outcome of the AI algorithm - Select a programming language and framework that suit your needs and skills, popular choices are Python, R, Tensorflow and Pytorch - Compare different algorithms and weigh their pros and cons, considering the resources and complexity involved - Write the code for the algorithm, using appropriate data structures and logic - Run the code on various inputs and cases, and fix any errors or bugs - Measure the algorithm's performance and accuracy, and compare it with your benchmarks - Deploy the algorithm to production if it meets your expectations - Continuously monitor user feedback and update the algorithm accordingly

To program artificial intelligence (AI) algorithms from scratch, start by gaining a foundational understanding of AI concepts and choosing a suitable programming language, such as Python. Familiarize yourself with AI libraries like TensorFlow and PyTorch. Select a specific AI algorithm based on your project goals, implement it step by step, and test thoroughly with relevant datasets. Stay updated on AI advancements, participate in communities for feedback, and gradually progress from simpler to more complex algorithms. Practical experience and continuous learning are key to mastering AI programming, and platforms like GitHub and Kaggle provide valuable resources for hands-on practice.

In my experience for creating an AI model from scratch involves doing an appropriate EDA.. (Processing of Data) and choosing the proper algorithm that will fit your Problem statement.. therefore a good knowledge of all the Algorithm is must.. Either if it is going to be SVM, Random forest or Logistic Regression a complete understanding of these algorithm is needed, so that we can train our data sets to obtain the higher accuracy. Hyper parameters tuning is also an important factor to achieve efficiency.

Training from scratch can be expensive and time consuming. Once you identify the goal or need as well as the data: - Consider if fine tuning an existing AI with your data can be an adequate solution. - ensure filtering of the data to have high quality training. Work with small language models (<2B) has shown that filtering and designing synthetic training data for a small model with a specific functional purpose can eliminate the need for scale and perform as well as larger models on benchmarks - consider security needs: ask whether the purpose and the data require that you ensure security more than usual. If so, do the research and avoid methods that can be reverse engineered to extract the data with common jailbreaks or other methods.

The AI world is vast, so pick your poison (er, algorithm)! Popular choices include: Linear Regression: Predict things like house prices based on size and location. Easy to grasp, a great gateway to more complex models. K-Nearest Neighbors: Find similar items based on existing data. Think recommending products based on past purchases. Perceptron: This OG algorithm lays the groundwork for neural networks. It learns to classify things like "cat" or "not cat" from images.

Après avoir établi des bases solides, la seconde étape dans la création d'algorithmes d'IA consiste à comprendre le problème et les données. Définissez clairement les objectifs, la portée, les contraintes et les limites du projet. Collectez et analysez les données, en déterminant le type, la quantité, ainsi que les méthodes de prétraitement, de nettoyage et de partitionnement en ensembles d'entraînement, de validation et de test.

Training models and validating them is a daunting task but can be made manageable if you have good data. The key to good data is to either buy it from a source that you trust or to curate it yourself. For example, in image contexts you would need lots of images and accurate labeling, often by hand for images that don’t have labels. It’s also important to consider storage, since large datasets like this tend to be too much for one machine. This means you’ll probably want MLOps or data engineering to be involved to help scale the curation and access to data for training and validation. Often spark or big data tools become important depending on how large the dataset is

Developing and assessing models present considerable difficulties, but successful oversight is attainable with top-notch data. The pivotal factor for quality data lies in either procuring it from a trusted source or meticulously curating it internally. For instance, in the context of images, an extensive collection and precise labeling, often manually done for unlabeled images, are essential. Storage considerations are paramount, particularly when dealing with voluminous datasets that may surpass the capacity of a single machine. Consequently, the involvement of MLOps or data engineering becomes imperative to facilitate the scalability of data curation and access for both training and validation purposes.

Whether you choose transformers, diffusion, deep RL, or other common methods consider a number of options: - black box architecture: sometimes you don’t need to train AI from scratch, you can use an existing model in an architecture that you design, eg with different calls to the same model, or combining different algorithms. - Fine-tuning: some LLMs allow the user to enter their own data in order to fine-tune an existing model. - White box solutions: you can reuse parts of an existing model, perhaps outputs from middle layers, eg by adding some components before the output, allowing more flexibility and efficiency in creating a new architecture. - train small models with curated data: small models can be powerful with well filtered data

Choisir la bonne technique d'IA est la troisième étape cruciale dans la programmation d'algorithmes d'IA. Les options, telles que l'apprentissage automatique, le traitement du langage naturel, la vision par ordinateur, et la robotique, offrent des approches spécialisées pour résoudre des problèmes allant de l'apprentissage à partir de données à la compréhension visuelle et à l'interaction physique avec le monde.

Most of the time at this point deep learning is likely the best choice for most applications. There are some cases like clustering or classification that might make sense to use more basic stuff like k means, but often these simple algorithms are used to validate the basic structure before being wrapped into deep learning. The advantage of deep learning is that you can rely on the training process to find features and feature extraction rather than trying to calculate those features yourself and dofeature engineering.

Choisir la bonne technique d'IA, c'est comme choisir le bon outil pour un travail spécifique. Voici les principales options : Apprentissage automatique 🧠 : Apprendre à partir de données, bon pour les grandes quantités de données. Traitement du langage naturel 🗣️ : Comprendre et parler les langues humaines, pour la reconnaissance vocale et l'analyse de texte. Vision par ordinateur 👁️ : Comprendre les images, utile pour détecter des objets ou des visages. Robotique 🤖 : Créer des machines intelligentes pour agir dans le monde réel. Choisis la branche qui correspond le mieux à ton projet. Ensuite, tu pourras plonger plus profondément dans cette technique.

Une fois les bases établies, la troisième étape dans la création d'algorithmes d'IA consiste à choisir une technique adaptée. Les branches principales comprennent l'apprentissage automatique, le traitement du langage naturel, la vision par ordinateur et la robotique. L'apprentissage automatique se concentre sur l'amélioration des performances sans programmation explicite, le traitement du langage naturel traite de l'interaction homme-machine linguistique, la vision par ordinateur comprend la compréhension visuelle, et la robotique combine matériel et logiciel pour créer des machines interactives avec le monde physique.

At this point if you have data and know the language it should be pretty basic to make the algorithm part. Honestly most of the time the hard part is getting the training and test sets ready. If that code is done, the algorithm is likely going to be a couple lines of iterating over the training data and evaluation of how well the model performs. Honestly this should be the shortest step if you’ve done the data plumbing properly

La cinquième étape dans la création d'algorithmes d'IA consiste à entraîner et ajuster l'algorithme. Fournissez vos données, permettez à l'algorithme d'apprendre et d'ajuster ses paramètres. Mesurez les performances avec des indicateurs tels que l'exactitude et la précision. Utilisez des techniques telles que la validation croisée et l'optimisation des hyperparamètres pour améliorer les résultats.

Systematic evaluation is a crucial part of developing an AI algorithm. This is important for evaluating function, robustness, security, and harm reduction. -Function: evaluation could vary from held out data/tasks, to benchmarks, and iterative or interactive evaluation. Consider the following when choosing What is the main purpose of the AI you’re building? Which existing benchmarks you can select to evaluate its performance? - Robustness: vary the tasks and evaluate your robustness of your agent. Careful design here helps you evaluate its response to unforeseen or tricky demands, & helps harm reduction. - security: do you need to protect your training set? Then evaluate with existing methods that trick AI to reveal its training set.

This part can be very painful to get wrong. Before doing any long term training make sure the basic structure is working as expected You should do your best to make training fast by using powerful machines, generally ones with GPU support, and parallel processing where it is possible. Pay close attention to the loss on your test validation to make sure training cycles are showing improved performance between batches. If the loss starts to flatten out you might need to fine tune hyper parameters to keep improving performance against your test sets. Its also important to use samples that are not trained on when doing validation. Otherwise you run the risk of over fitting or forward looking models

La dernière étape consiste à déployer l'algorithme d'IA, l'intégrer à l'application, et le maintenir. Offrez-le aux utilisateurs, assurez des mises à jour régulières pour une performance fiable. Exploitez des outils innovants comme les services cloud, les frameworks web et les conteneurs pour faciliter ce processus dynamique.

The key to programming AI algorithms from scratch is simply not to do it. You wouldn't reinvent your transportation. Stand on the shoulder of giants and use the tools necessary. Unless you are in a highly research oriented role this doesn't make much sense.. and if you're in a position where you need to write your own custom AI from scratch then chances are you aren't on LinkedIn reading these articles on how to do it.

Perhaps the first question should be "do you need to write the algorithm from scratch?" Why? What would be the advantage? What would your implementation have that others miss? One of the big advantage of many tools is that they have streamlined parts of the process (eg layer construction in NN, initialisation, efficient backpropagation ...) so you can focus on the bits which add value in your specific use case (eg the architecture)

Actually you don't need to reinvent the weel and it takes a lot of time and computation power to make an AI from scratch there's lot of opensource AI we can just carve it into a new tool that's the better way even MNCs are doing like that.