Mlearning.ai

APRIL 8, 2023

The earlier models that were SOTA for NLP mainly fell under the traditional machine learning algorithms. These included the Support vector machine (SVM) based models. 2014) Significant people : Geoffrey Hinton Yoshua Bengio Ilya Sutskever 5.

Natural Language Processing 98

Natural Language Processing Algorithm Machine Learning Machine Learning 98

Becoming Human

MARCH 16, 2023

Overhyped or not, investments in AI drug discovery jumped from $450 million in 2014 to a whopping $58 billion in 2021. AI drug discovery is exploding.

AI 139

AI AI Artificial Intelligence Artificial Intelligence 139

ML Review

MARCH 5, 2019

Crafting a dataset The number of papers added to ArXiv per month since 2014. As a starting point for our lofty goal, we used the arxiv-sanity code base (created by Andrej Karpathy) to collect ~50,000 papers from the ArXiv API released from 2014 onwards and which were in the fields of cs. Every month except January.

AI 52

AI AI Support Vector Machines Natural Language Processing 52

Mlearning.ai

FEBRUARY 15, 2023

Uysal and Gunal, 2014). Prediction of Solar Irradiation Using Quantum Support Vector Machine Learning Algorithm. Introduction In natural language processing, text categorization tasks are common (NLP). Depending on the data they are provided, different classifiers may perform better or worse (eg. Dönicke, T.,

Cross Validation 52

Cross Validation Decision Trees Algorithm Natural Language Processing 52

Mlearning.ai

JUNE 8, 2023

Sentence embeddings can also be used in text classification by representing entire sentences as high-dimensional vectors and then feeding them into a classifier. How can we make the machine draw the inference between ‘crowded places’ and ‘busy cities’?

Machine Learning 52

Machine Learning Machine Learning Clustering Database 52

PyImageSearch

NOVEMBER 13, 2023

Step #4: Classify each proposal using the extracted features with a Support Vector Machine (SVM). The original Faster R-CNN paper used VGG (Simonyan and Zisserman, 2014) and ZF (Zeiler and Fergus, 2013) as the base networks. Today, we would typically swap in a deeper, more accurate base network, such as ResNet ( He et al.,

Deep Learning 119

Deep Learning Deep Learning Algorithm Support Vector Machines 119