What neural network technologies and trends should you know to stay up-to-date in software development?

Nowadays, Deep Neural Networks are a hot topic since businesses embrace them as a way to intelligently automate processes. As a result, you should mainly be familiar with CNN, Autoencoders, Transformers, LSTM. Study and review network parameters and associated them with concepts such as vanishing gradient, back propagation, and implications for having large numbers of parameters.

One rapidly evolving neural network architecture that software developers should keep an eye on is the transformer model. Transformers, which utilize attention mechanisms have become ubiquitous in natural language processing tasks. Variants like BERT, GPT-3, and DALL-E have demonstrated impressive capabilities in language understanding, text generation, and image generation. The scalability and flexibility of transformers make them well-suited for a variety of software applications. For example, GitHub's Copilot coding assistant leverages a transformer model trained on public code. Transformers can also enhance software testing, API development, and documentation generation.

I would say that Tensorflow, pyTorch and MXNet are very important. If you want to work with traditional ML algorithms, scikit-learn is the most popular one. H20 is a very good library and if you work with pyspark, SparkML is a must.

Deep Neural Networks (DNN) are great for different problems; however they need tons of data in order to yield good results. For computer-vision and NLP related tasks, DNN are the way to go. However, if your problem is from a different domain, ensemble methods such as XGBoost or Random Forest are a very good alternative. For regression problems, Prophet is a good library, if your time series problems exhibit stationarity, peak changes due to some external events. In any case, remember to start creating base line models with simple / explainable algorithms whenever is possible before jumping into DNN. Recall data and algorithms are the same, to make substantial difference you must do feature engineering.

Here are some best practices and insights I've found helpful in my work: - High-quality, clean, and well-organized data is fundamental (proper preprocessing, including data normalization and augmentation) - Choose the appropriate neural network architecture for your task. Consider the size and complexity of the model based on available resources. - Split your data into training, validation, and test sets to assess model performance. - Proper logging and monitoring of model training to detect issues early and track progress. - Combine multiple models to improve predictive accuracy, reducing the risk of overfitting. - Consider using pre-trained models and fine-tuning for tasks with limited data. - Consider hardware acceleration if necessary.

Ethic always comes first. From there be sure to have explainability, business users will ask what is going on behind scenes. Explain your results based on the metrics business wants to optimize. Version your work at different levels (data preparation, feature engineering and model tunning, model evaluation) therefore you can compare and make proper model selection. Monitor your model performance once in production.

There are many different research lines., I would suggest to work, read, experiment in feature engineering, how to represent my data to provide a better understanding / enhance capabilities of the ML algorithm.