Machine Learning on Encrypted Data with CrypTen
Learn to develop machine learning models on encrypted data using CrypTen, a PyTorch-based framework for secure computing. Explore feature aggregation, data labeling, dataset augmentation, and model hiding techniques.
Getting Started with Automatic Differentiation
Explore automatic differentiation in Python, its applications in optimization and computational art, using libraries like JAX, TensorFlow, and PyTorch. Gain intuition for derivatives and gradients.
Practical Deep Learning for Data Scientists
Hands-on PyTorch and GPU Deep Learning tutorial for data scientists. Build common architectures, evaluate performance, and leverage compute resources effectively. Ideal for those with ML basics seeking practical DL experience.
From NumPy to PyTorch - A Story of API Compatibility
Explore PyTorch's approach to NumPy compatibility, its importance, challenges, and necessary divergences in this insightful discussion on API design and data science workflows.
Intro to Hugging Face - Fine-Tuning BERT for NLP Tasks
Hands-on tutorial for fine-tuning BERT models using Hugging Face and PyTorch. Learn to adapt pre-trained NLP models for sentiment analysis, gaining practical skills in deep learning and confidence in navigating open-source tools.
Breaking Boundaries - Advancements in High-Performance AI/ML through PyTorch's Python Compiler
Explore PyTorch's innovative Python compiler, designed to enhance AI/ML performance while maintaining Python roots. Learn about the development process, challenges, and significant performance improvements achieved.
An Introduction to PyTorch & Autograd
Explore PyTorch's dynamic computation graphs and Autograd package for efficient deep learning, with practical examples and insights into GPU processing and algorithmic differentiation.
Deep Learning with PyTorch for Fun and Profit
Explore practical deep learning applications using PyTorch, including style transfer, speech generation, and text generation. Learn about data preparation, hardware setup, and creating neural network models for creative projects.
How to Train an Image Classifier Using PyTorch
Learn to build an image classifier using PyTorch in this hands-on tutorial. Covers data preparation, model selection, training techniques, and evaluation, with practical tips and code examples for immediate implementation.
Classification Based on Missing Features in Deep Convolutional Neural Networks
Explore a novel approach to enhance neural network accuracy and robustness in adversarial situations, focusing on classification based on missing features in deep convolutional models.
Train, Serve, Deploy - Story of a NLP Model ft. PyTorch, Docker, Uwsgi and Nginx
Learn to build, train, and deploy NLP translation models using PyTorch, Flask, Docker, uWSGI, and Nginx. Covers data preprocessing, model training, web app creation, containerization, and production serving techniques.
Speeding Up the Deep Learning Development Life Cycle for Cancer Diagnostics
Optimizing deep learning development for cancer diagnostics: strategies to accelerate iteration, from task prioritization to GPU parallelization, enhancing model robustness across diverse laboratory settings.
Private Data Anonymization with Python - Fundamentals
Explore techniques for anonymizing legal documents using Python, including entity recognition, masking, and pseudoanonymization. Learn to apply AI models and text mining for improved data privacy compliance.
Coding Deep Learning
Explore deep learning fundamentals, including matrix algebra and calculus, and get practical tips for using frameworks like Keras, PyTorch, and TensorFlow in this informative session.
Production Multi-node Jobs with Gang Scheduling, K8s, GPUs and RDMA
Explore distributed execution of DL/ML jobs in Kubernetes, covering gang scheduling, GPU acceleration, and high-speed networking for improved performance and scalability.
Bring Batch Capability Into Kubernetes, Using AI and Big Data as an Example
Explore advanced scheduling features for batch workloads in Kubernetes, focusing on AI and big data applications using frameworks like TensorFlow and Spark. Learn about fair-share scheduling and the Volcano project.
Accelerate and Autoscale Deep Learning Inference on GPUs with KFServing
Explore KFServing for efficient GPU-based deep learning inference, focusing on BERT models. Learn about scalability, performance optimization, and real-world applications in financial data processing.
Deep Learning with PyTorch
Comprehensive introduction to deep learning using PyTorch, covering fundamentals, computer vision applications, and practical model creation for AI enthusiasts and developers.
Training BERT - Train With Masked-Language Modeling
Learn to fine-tune BERT models using masked language modeling in PyTorch for domain-specific NLP tasks, enhancing performance beyond out-of-the-box capabilities.
Sentence Similarity With Transformers and PyTorch
Learn to measure semantic similarity between sentences using BERT and PyTorch. Explore tokenization, vector creation, and cosine similarity calculation for powerful NLP applications.
Pytorch Neural Style Transfer Tutorial
Learn to implement Neural Style Transfer from scratch in PyTorch, covering theory and practical coding to blend content and style images for artistic results.