A practical guide to implementing a hyperparameter tuning script for machine learning models, based on real-world experience from Taboola's engineering team.
A comprehensive overview of policy gradient algorithms in reinforcement learning, covering key concepts, notations, and various methods.
Explains the Gated Multimodal Unit (GMU), a deep learning architecture for intelligently fusing data from different sources like images and text.
An introductory guide to Reinforcement Learning (RL), covering key concepts, algorithms like SARSA and Q-learning, and its role in AI breakthroughs.
Explores the R-CNN family of models for object detection, covering R-CNN, Fast R-CNN, Faster R-CNN, and Mask R-CNN with technical details.
Practical tips for training sequence-to-sequence models with attention, focusing on debugging and ensuring the model learns to condition on input.
Explores classic CNN architectures for image classification, including AlexNet, VGG, and ResNet, as foundational models for object detection.
A retrospective on the transformative impact of deep learning over the past five years, covering its rise, key applications, and future potential.
A recap of PyData Warsaw 2017, covering key talks, new package announcements, and analytics on the conference's international attendees.
Argues that speech recognition hasn't reached human-level performance, highlighting persistent challenges with accents, noise, and semantic errors.
Explores applying information theory, specifically the Information Bottleneck method, to analyze training phases and learning bounds in deep neural networks.
Explains the math behind GANs, their training challenges, and introduces WGAN as a solution for improved stability.
A comparison of PyTorch and TensorFlow deep learning frameworks, focusing on programmability, flexibility, and ease of use for different project scales.
Explores the importance of interpreting ML model predictions, especially in regulated fields, and reviews methods like linear regression and interpretable models.
A developer explores using deep learning and sequence-to-sequence models to train a chatbot on personal social media data to mimic their conversational style.
An introduction to deep learning, explaining its rise, key concepts like CNNs, and why it's powerful now due to data and computing advances.
A review and tips for Georgia Tech's OMSCS CS6476 Computer Vision course, covering content, assignments, and personal experience.
A guide for beginners on how to start learning deep learning using the Keras library, including recommended resources and prerequisites.
A deep dive into applying deep learning techniques to Natural Language Processing (NLP), covering word vectors and research paper summaries.
A speculative look at future technologies including brain-computer interfaces, quantum computing, AI, and bio-implants.
