Deep learning has had a great impact on various fields of computer science by enabling data-driven representation learning in a decade. Because science and technology policy decisions for a nation can be made on the impact of each technology, quantifying research impact is an important task. The number of citations and impact factor can be used to measure the impact for individual research. What would have happened without the research, however, is fundamentally a counterfactual phenomenon. Thus, we propose an approach based on causal inference to quantify the research impact of a specific technical topic. We leverage difference-in-difference to quantify the research impact by applying to bibliometric data. First, we identify papers of a specific technical topic using keywords or category tags from Microsoft Academic Graph, which is one of the largest academic publication dataset. Next, we build a paper citation network between each technical field. Then, we aggregate the cross-field citation count for each research field. Finally, the impact of a specific technical topic for each research field is estimated by applying difference-in-difference. Evaluation results show that deep learning significantly affects computer vision and natural language processing. Besides, deep learning significantly affects cross-field citation especially for speech recognition to computer vision and natural language processing to computer vision. Moreover, our method revealed that the impact of deep learning was 3.1 times of the impact of interpretability for ML models.

By now, the whole world is chattering about'ChatGPT', Bard and other AI chatbots. AI or'Artificial Intelligence' is the concept that is powering these chatbots. 'Artificial Intelligence' as the name suggests is intelligence in machines which seek to mimic human intelligence. AI in chatbot is given the super concoction of computer science knowledge and large data sets to make it give answers on any topic like a super human dictionary. One popular example of AI is the'ChatGPT' chatbot that made its appearance in November of 2022 and was adopted by all in the tech community.

One of these target audiences is university students(undergraduate or graduate) learning about machine learning, including those who are beginning a career in deep learning and artificial intelligence research. The other target audience is software engineers who do not have a machine learning or statistics background, but want to rapidly acquire one and begin using deep learning in their product or platform. Basically, if you are interested in reading this book and haven't been turned off by the content of this post, the book is likely for you. The book starts off covering the required background for understanding later material, along with historical context and elementary explanations of the technical concepts. In fact, the entire first part of the book is dedicated to building the technical foundation required to study deep learning.

One of these target audiences is university students(undergraduate or graduate) learning about machine learning, including those who are beginning a career in deep learning and artificial intelligence research. The other target audience is software engineers who do not have a machine learning or statistics background, but want to rapidly acquire one and begin using deep learning in their product or platform. Basically, if you are interested in reading this book and haven't been turned off by the content of this post, the book is likely for you. The book starts off covering the required background for understanding later material, along with historical context and elementary explanations of the technical concepts. In fact, the entire first part of the book is dedicated to building the technical foundation required to study deep learning.