A few years ago, Aspiring Minds created a stir when it published a report that stated that 95% of engineers were not fit for software development jobs. The report said only 1.4% of them could write functionally correct code. Computer programs are the foundation of applied computer science, and writing them is an important skill for students to have. Every IT company in India looks for fresh graduates with programming skills. Since the companies are convinced that new computer science graduates don't have these skills, they screen them for logical reasoning/aptitude tests and train them in programming languages.

Sequential modelling entails making sense of sequential data, which naturally occurs in a wide array of domains. One example is systems that interact with users, log user actions and behaviour, and make recommendations of items of potential interest to users on the basis of their previous interactions. In such cases, the sequential order of user interactions is often indicative of what the user is interested in next. Similarly, for systems that automatically infer the semantics of text, capturing the sequential order of words in a sentence is essential, as even a slight re-ordering could significantly alter its original meaning. This thesis makes methodological contributions and new investigations of sequential modelling for the specific application areas of systems that recommend music tracks to listeners and systems that process text semantics in order to automatically fact-check claims, or "speed read" text for efficient further classification.

This paper demonstrates that by fine-tuning an autoregressive language model (GPT-Neo[1], [2]) on appropriately structured step-by-step demonstrations, it is possible to teach it to execute a mathematical task that has previously proved difficult for Transformers - longhand modulo operations - with a relatively small number of examples. Specifically, we fine-tune GPT-Neo to solve the numbers__div_remainder task from the DeepMind Mathematics Dataset; Saxton et al. [3] reported below 40% accuracy on this task with 2 million training examples. We show that after fine-tuning on 200 appropriately structured demonstrations of solving long division problems and reporting the remainders, the smallest available GPT-Neo model achieves over 80% accuracy. This is achieved by constructing an appropriate dataset for fine-tuning, with no changes to the learning algorithm. These results suggest that fine-tuning autoregressive language models on small sets of well-crafted demonstrations may be a useful paradigm for enabling individuals without training in machine learning to coax such models to perform some kinds of complex multi-step tasks.

The purpose of this project is to provide a comprehensive and yet simple course in Machine Learning using Python. Machine Learning, as a tool for Artificial Intelligence, is one of the most widely adopted scientific fields. A considerable amount of literature has been published on Machine Learning. The purpose of this project is to provide the most important aspects of Machine Learning by presenting a series of simple and yet comprehensive tutorials using Python. In this project, we built our tutorials using many different well-known Machine Learning frameworks such as Scikit-learn.

Create your own chatbots using the world's most popular chatbot language. This course is designed for people with absolutely no knowledge of Artificial Intelligence Markup Language (AIML). It guides you step by step and teaches you how to create a chatbot using the world's most popular chatbot language. From the very beginning to more advanced features, take it at your own pace, practice and learn from Steve Worswick, the 5 times holder of the Loebner Prize.

Female tech pioneers from the UAE have described their desire to get more girls to learn about artificial intelligence and coding. They spoke in an online meeting of thought leaders, organised by the British Embassy UAE on Tuesday. Gamification and using the power of social media were two suggestions on how to encourage more young people to consider a career in tech. Emirati student, Fatima Ali Aldhuhoori, who was part of her school's prize-winning robotics team and Radhika Iyer, who studied in the UAE before moving to the UK and winning the Amazon Longitude Explorer Prize for tech design, discussed how much they enjoyed using computer science to solve problems. Globally, these two women are outliers for their generation.

What is Python and why is it so popular? This is a commonly Googled question today, even as more people turn to software programming / software development as a career option. There are many coding languages available today. But Python leads the pack. What is the reason behind the increasing demand for programmers proficient in Python?

In this article, we've listed all Machine Learning and Deep Learning courses by Andrew Ng, an excellent teacher from Standford University, and a tech-entrepreneur. The Machine Learning and Deep Learning courses given below are all available on Coursera in case you are interested in enrolling in any one of them. The Machine Learning course by Stanford and popularized by Andrew Ng's teaching is the best certification course in Machine Learning you can go for. The course is 11 weeks long and covers almost everything that you need to know about Machine Learning with great examples and assignments. The course has a 4.9/5 average rating from over 160,000 student ratings.

The average American consumes over 58 pounds of beef and 141 pounds of milk per year, but cattle farming is a deeply resource-intensive process with significant impacts on land use and carbon emissions – so any gains in efficiency are highly prized. Now, researchers at the University of Florida (UF) have used the university's powerful new HiPerGator AI supercomputer to help ranchers identify the highest-yield livestock. This June, UF made waves when HiPerGator AI, which delivers 17.2 Linpack petaflops, debuted on the Top500 list as the world's third most powerful publicly ranked supercomputer at an educational institution and debuted on the Green500 list as the world's second most efficient publicly ranked supercomputer. So when researchers from UF's Institute of Food and Agricultural Sciences (IFAS) set out to improve cattle yields in a smarter way, they turned to supercomputer-powered AI. "AI has rapidly emerged as a powerful approach in animal genomics and holds great promise to integrate big data from multiple biological layers, leading to accurate prediction of future traits – for example, meat yield," said Raluca Mateescu, a professor of animal science at UF. "My research group is investigating the use of AI methods to develop approaches to accurately predict the value of certain genes. Ultimately, we plan to provide more effective strategies to improve animal productivity."

Data Science, which is also known as the sexiest job of the century, has become a dream job for many of us. But for some, it looks like a challenging maze and they don't know where to start. If you are one of them, then continue reading. In this post, I'll discuss how you can start your journey of Data Science from scratch. I'll explain the following steps in detail.