A curated list of public dataset repositories for machine learning and deep learning projects, including sources for computer vision, NLP, and more.
A detailed review of the book 'Deep Learning with PyTorch,' covering its structure, content, and suitability for students and practitioners.
A review of the book 'Deep Learning with PyTorch', covering its structure, content, and suitability for students and beginners in deep learning.
An analysis of GPT-3's capabilities, potential for misuse in generating fake news and spam, and its exclusive licensing by Microsoft.
Explores whether deep learning creates a new kind of program, using the philosophy of operationalism to compare it with traditional programming.
Key takeaways from RecSys 2020 conference, focusing on ethics, bias, sequence models, and notable papers in recommender systems.
A chronological survey of key NLP models and techniques for supervised learning, from early RNNs to modern transformers like BERT and T5.
An overview of Neural Architecture Search (NAS), covering its core components: search space, algorithms, and evaluation strategies for automating AI model design.
An introductory chapter on machine learning and deep learning, covering core concepts, categories, and terminology from a university course.
An introductory chapter on machine learning and deep learning, covering core concepts, categories, and the shift from traditional programming.
Summary of key application-agnostic talks from Spark+AI Summit 2020, focusing on scaling and optimizing deep learning models.
An analysis of OpenAI's GPT-3 language model, focusing on its 175B parameters, in-context learning capabilities, and performance on NLP tasks.
Explores video segmentation techniques like rotoscoping and green screens used in Hollywood VFX, comparing them to modern AI models like Deeplab v3+.
A tutorial on using AWS AutoGluon, an AutoML library, to build an object detection model with minimal code.
A comparative analysis of the underlying architecture and design principles of TensorFlow and PyTorch machine learning frameworks.
A technical tutorial explaining the fundamentals of Convolutional Neural Networks (CNNs) by manually calculating layers from the classic LeNet-5 architecture.
Explores the human effort behind AI training data, covering challenges of data annotation and techniques like transfer learning to reduce labeling workload.
A personal blog about machine learning, data annotation projects, and professional experiences in deep learning and AI product development.
A developer shares their personal learning journey and syllabus for mastering Python, Machine Learning, and Deep Learning in 2020.
A review of 'Architects of Intelligence,' a book featuring interviews with 23 leading AI researchers and industry experts.
