In last weeks, many of us have been experimenting with the powerful Azure OpenAI APIs, either in the playground or via REST API or Python SDK. However, those APIs alone are not sufficient to build powerful applications.

Summary This year the International Conference on Natural Language Generation (INLG) will feature an award for the paper with the best evaluation. The purpose of this award is to provide an incentive for NLG researchers to pay more attention to the way they assess the output of their systems. This essay provides a short introduction to evaluation in NLG, explaining key terms and distinctions. How can I evaluate my system? It is hard to say in general how you should evaluate your NLG system.

Can you measure the temperature inside the core of a nuclear reactor to make sure the nuclear reaction is controlled? It certainly is too hot for any thermostat manufactured to date. The closest one can go is to measure the temperature of a surface close to the core and estimate the temperature inside it. Let us consider another example to internalize this concept where direct measurement of a phenomenon is not possible – can you measure the exact position of a flying object using radar technology considering variable air density, wind direction, and wind speed? What if the wind changed direction?

Semi-supervised learning (SSL) is the branch of machine learning that aims to improve learning performance by leveraging unlabeled data when labels are insufficient. Recently, SSL with deep models has proven to be successful on standard benchmark tasks. However, they are still vulnerable to various robustness threats in real-world applications as these benchmarks provide perfect unlabeled data, while in realistic scenarios, unlabeled data could be corrupted. Many researchers have pointed out that after exploiting corrupted unlabeled data, SSL suffers severe performance degradation problems. Thus, there is an urgent need to develop SSL algorithms that could work robustly with corrupted unlabeled data. To fully understand robust SSL, we conduct a survey study. We first clarify a formal definition of robust SSL from the perspective of machine learning. Then, we classify the robustness threats into three categories: i) distribution corruption, i.e., unlabeled data distribution is mismatched with labeled data; ii) feature corruption, i.e., the features of unlabeled examples are adversarially attacked; and iii) label corruption, i.e., the label distribution of unlabeled data is imbalanced. Under this unified taxonomy, we provide a thorough review and discussion of recent works that focus on these issues. Finally, we propose possible promising directions within robust SSL to provide insights for future research.

One of the most interesting avenues unlocked by Artificial Intelligence and Analytics is Facial Recognition, powered by AI & ML algorithms. We see this application in use on a daily basis- in smartphones, security stations etc. In this introductory blog, I will provide a quick walkthrough of the technology involved in Facial Recognition. Facial Recognition is a recognition technique used to detect faces of individuals whose images are saved in the data set. Despite the fact that other methods of identification may be more accurate, Facial Recognition has always remained a significant research point because of its non-invasive nature and its ease of use. There are various algorithms that can perform Facial Recognition, but their accuracy might vary.

Let's start the journey of understanding how face recognition works with an example: I take an example of the Face Aligned Face Dataset from Pinterest. This dataset contains 10,770 images for 100 people. All images are taken from'Pinterest' and aligned using the dlib library. In this problem, we use a pre-trained model trained on Face recognition to recognize similar faces. Here, we are particularly interested in recognizing whether two given faces are of the same person or not.

Data adventure, which started with data mining concept, has been in a continuous development with introducing different algorithms. There are many applicable algorithms in AI. Besides, AI is actively used in marketing, health, agriculture, space, and autonomous vehicle production for now. Data mining is divided into different models according to fields in which it is used. These models can be grouped under four main headings as a value estimation model, database clustering model, link analysis, and difference deviations.

The outer layer of the cerebral hemisphere is termed the cerebral cortex. This is inter-connected via pathways that run sub-cortically. It is these connections as well as the connections from the cerebral cortex to the brainstem, spinal cord and nuclei deep within the cerebral hemisphere that form the white matter of the cerebral hemisphere. The deep nuclei include structures such as the basal ganglia and the thalamus. The main difference between cerebrum and cerebral cortex is that cerebrum is the largest part of the brain whereas cerebral cortex is the outer layer of the cerebrum.

You'll start by absorbing the most valuable PyTorch basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python-based data science in real -life. After taking this course, you'll easily use packages like Numpy, Pandas, and PIL to work with real data in Python along with gaining fluency in PyTorch.

Now, let's get to our main topic. What can you do with Python? In this part, I try to prepare roughly some examples that you can do with python. Developing web applications is one of the most in-demand skills. In here, Python present some toolkits and package.