Debugging AI: Strategies for Upcoming Talents
Gain insights into AI debugging strategies, from practical tips to handling complex models. Learn about data privacy, continuous monitoring, and effective techniques for junior engineers in ML systems.
DIY AI: Build and Train Your Own Model with PyTorch
Dive into building and training a custom AI model using PyTorch, Python, and Jupyter Labs. Learn step-by-step techniques to create a deployable model for Raspberry Pi or other computers.
Keras Tutorial With TensorFlow - Building Deep Learning Models With Python
Learn to build deep learning models using Keras and TensorFlow in Python. Covers data preparation, neural network construction, and practical implementation with hands-on examples.
Deep Learning with Keras
Explore deep learning with Keras, covering neural networks, face recognition, OpenPose, stock prediction, and more. Hands-on tutorials for practical AI applications.
Deep Learning from the Foundations
Explore advanced deep learning techniques, from model training to optimization, with hands-on lessons covering ULMFiT, Swift for deep learning, and more.
Practical Deep Learning for Coders - 2020
Learn essential deep learning skills for modern applications through hands-on coding exercises and comprehensive lessons, suitable for both beginners and experienced programmers.
Neural Networks and TensorFlow Crash Course
Comprehensive introduction to neural networks and TensorFlow, covering key concepts, data handling, model building, and practical applications in image and text classification.
Classifying Poses with ML5.js - Part 2
Learn to classify poses using PoseNet and ml5.js in this hands-on tutorial. Train a pose classifier, implement machine learning techniques, and explore practical applications in computer vision and gesture recognition.
PyTorch - Deep Learning with Python
Explore deep learning and neural networks using Python and PyTorch, covering data handling, model building, training, and analysis, with a focus on convolutional networks and GPU acceleration.
Mask R-CNN - Practical Deep Learning Instance Segmentation Tutorials
Learn to implement and train Mask R-CNN for instance segmentation using OpenCV and Python, covering installation, real-time applications, and custom object detection like potholes.
Amazon SageMaker Technical Deep Dive Series
Comprehensive deep dive into Amazon SageMaker, covering notebook instances, algorithms, custom models, training, deployment, tuning, and advanced features for efficient machine learning workflows.
End to End Machine Learning Project Implementation with Dockers, GitHub Actions and Deployment
Comprehensive guide to implementing a machine learning project, covering data analysis, model training, web application development, and deployment using Docker and GitHub Actions.
Data Validation and Model Training - Python Web App From Scratch With Justin Mitchel
Learn intermediate Python web app concepts: data validation, schema design, and model training. Enhance your skills in configuring databases, writing tests, and handling invalid data.
A Quick Guide to Machine Learning
Explore foundational machine learning concepts and their practical applications using Visual Studio Code. Learn through demos, model training, and discussions on responsible AI.
Machine Learning on Your Device - The Options
Explore TensorFlow's options for integrating machine learning into mobile apps and edge devices, from model training to deployment using TensorFlow Lite.
Denoising 3D Multi-Channel Scientific Images Using Noise2Void Deep Learning Approach
Learn to denoise 2D and 3D multichannel scientific images using Noise2Void deep learning, focusing on CZI files from ZEISS microscopes. Explore implementation, training, and practical applications for improved image quality.
Unpaired Image to Image Translation Using CycleGAN - An Introduction
Introduction to CycleGAN for unpaired image-to-image translation, covering key concepts, architecture, and training process. Explores discriminator and generator components, focusing on PatchGAN and instance normalization.
LoRA - Low Rank Adaptation for Parameter Efficient Fine-Tuning
Dive into parameter-efficient fine-tuning with LoRA, exploring low-rank matrices, adapters, and techniques to optimize neural network storage and performance while maintaining accuracy.
Open Technical Questions in Generative AI Copyright and Risk Mitigation
Explore critical technical challenges around copyright risks, mitigation strategies, and compensation mechanisms in generative AI models, with focus on quantification and practical solutions.
Distortion-Free Mechanisms for Language Model Provenance - Watermarking and Training Independence
Explore groundbreaking watermarking techniques and independence testing methods for language models, focusing on text generation provenance and model weight verification.
Prover-Verifier Games for Improving LLM Output Legibility
Explore how prover-verifier games enhance LLM output clarity and trustworthiness, focusing on mathematical problem-solving and human verification techniques.