TensorFlow — Brand Summaries
AI-powered summaries of 10 videos about TensorFlow.
10 summaries
Generative AI Fine Tuning LLM Models Crash Course
Fine-tuning large language models becomes practical on limited hardware when three ideas work together: quantization to shrink model weights,...
PyTorch: An Imperative Style, High-Performance Deep Learning Library
This paper asks a practical but foundational research question: can a deep learning framework deliver both (1) an imperative, Pythonic...
Lecture 4: Transfer Learning and Transformers (Full Stack Deep Learning - Spring 2021)
Transfer learning is the bridge that lets large, pre-trained neural networks work on small, task-specific datasets—first in computer vision, then in...
Jeremy Howard on Platform.ai and Fast.ai (Full Stack Deep Learning - March 2019)
Jeremy Howard argues that “augmented machine learning”—tight human–computer collaboration—beats fully automated ML pipelines for most practical...
Panel Discussion: Do I need a PhD to work in ML? (Full Stack Deep Learning - Spring 2021)
A PhD is not a universal requirement for working in machine learning; it depends on what kind of ML work someone wants to do and how employers...
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
This paper asks whether residual connections—introduced in prior work to improve optimization of very deep networks—provide additional benefits when...
Advances and Open Problems in Federated Learning
This paper, “Advances and Open Problems in Federated Learning” (Foundations and Trends® in Machine Learning, 2020), is a broad survey and research...
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
This paper asks a practical but high-impact question for automatic speech recognition (ASR): can we improve end-to-end speech recognition accuracy...
Deep Learning Frameworks
Deep learning frameworks can be judged along two practical axes: how pleasant they are for building models and how well they scale once those models...
DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials
This paper addresses a practical but increasingly central bottleneck in machine learning potentials (MLPs) for atomistic simulation: most MLP...