In an era of widespread influence of Natural Language Processing (NLP), there have been multiple research efforts to supplant traditional manual coding techniques with automated systems capable of generating solutions autonomously. With rapid research for code generation and a sole focus on large language models, there emerges a need to compare and evaluate the performance of transformer architectures based on several complexities of the model. This paper introduces the concept of a "A Comparative Study on Code Generation with Transformers," a model based on Transformer architecture, and NLP methodologies to automatically generate C++ source code for different varieties of problems. Here, a comparative study is performed to evaluate the robustness of transformer-based models on the basis of their architecture complexities and their capability to handle diverse problem sets, from basic arithmetic to complex computations.

In recent years, product categorisation has been a common issue for E-commerce companies who have utilised machine learning to categorise their products automatically. In this study, we propose an ensemble approach, using a combination of different models to separately predict each product's category, subcategory, and colour before ultimately combining the resultant predictions for each product. With the aforementioned approach, we show that an average F1-score of 0.82 can be achieved using a combination of XGBoost and k-nearest neighbours to predict said features.

Batch Norm is an essential part of the toolkit of the modern deep learning practitioner. Soon after it was introduced in the Batch Normalization paper, it was recognized as being transformational in creating deeper neural networks that could be trained faster. Batch Norm is a neural network layer that is now commonly used in many architectures. It often gets added as part of a Linear or Convolutional block and helps to stabilize the network during training. In this article, we will explore what Batch Norm is, why we need it and how it works.

Often while working with Artificial Neural Networks or other variations like Convolution Neural Networks or Recurrent Neural Networks, we want to visualize and create a diagrammatic representation of our compiled model. There are a few packages readily available in python that can create a visual representation of our Neural Network Models. The first three packages can be used even before a model is trained (the model needs to be defined and compiled only); however, Tensor Boards requires the user to train the model on accurate data before the architecture can be visualized. We don't need to install the "Tensor Board" and "Keras Model Plot" separately. This will come with the initial installation of Tensorflow & Keras.

Recent research in machine learning (ML) has led to significant progress in various fields, including scientific applications. However, there are limitations that need to be addressed to ensure the validity of new models, the quality of testing and validation procedures, and the actual applicability of the developed models to real-world problems. These limitations include unfair, subjective, and unbalanced evaluations, not necessarily intentional yet there, the use of datasets that don't properly reflect real-world use cases (for example that are "too easy"), incorrect ways to split datasets into training, testing, and validation subsets, etc. In this article I will discuss all these points, using examples from the domain of biology which is being revolutionized by ML methodologies. Along the way I will also briefly touch on the interpretability of ML models, which is today very limited but very important because it could help clarify many of the aspects discussed in the first part of the article regarding the limitations that need to be addressed.

All the sections of the Introduction should be balanced, thus you should reserve the same number of paragraphs to all of them, more or less. Up to now, you have written a draft of the abstract and the Introduction and Related Work Sections. You are ready to give a structure to your paper. I strongly encourage you to take again the Introduction and split it into paragraphs. Then, you could add one section for each paragraph. Remind that while writing the paper, you can add, delete or modify any section you have already written.

While some of us were lucky enough to work only with great team leads, most of us have had both great and terrible experiences. And although terrible leadership can make the team members' life horrible, bitter experiences foster great team leads from the team members -- helping them learn what behaviours to avoid. Technical management of software engineering projects is very established, with multiple tools and techniques at the disposal of a team lead, such as Agile. Meanwhile, machine learning projects, where accurately predicting timelines, outcomes of the tasks, and task feasibility are challenging, are hard to fit into these paradigms. Navigating projects with high uncertainty at every step requires skills and knowledge that machine learning team leads need to gain through experience.

We seem to be in the golden era of AI. Every week there is a new service that can do anything from creating short stories to original images. These innovations are powered by machine learning. We use powerful computers and vast amounts of data to train these models. The problem is, this process leaves us with a poor understanding of how they actually work.

Click here to read the full story with my Friend Link! Breast cancer is the second most common cancer in women and men worldwide. In 2012, it represented about 12 percent of all new cancer cases and 25 percent of all cancers in women. Breast cancer starts when cells in the breast begin to grow out of control. These cells usually form a tumor that can often be seen on an x-ray or felt as a lump. The tumor is malignant (cancer) if the cells can grow into (invade) surrounding tissues or spread (metastasize) to distant areas of the body.

Recently I have written an article about the risks of using the train_test_split() function provided by the scikit-learn Python package. That article has raised a lot of comments, some positives, and others with some concerns. The main concern in the article was that I used a small dataset to demonstrate my theory, which was: be careful when you use the train_test_split() function, because the different seeds may produce very different models. The main concern was that the train_test_split() function does not behave strangely; the problem is that I used a small dataset to demonstrate my thesis. In this article, I try to discover which is the performance of a Linear Regression model by varying the dataset size.