# Deep learning Projects at AI Tinkerers > Canonical HTML: https://aitinkerers.org/technologies/deep-learning > Markdown URL: https://aitinkerers.org/technologies/deep-learning.md > Technology record last updated: 2026-02-26T19:57:49Z > Generated: 2026-09-21T01:34:46Z Deep learning uses multilayered neural networks (DNNs) to automatically learn complex, hierarchical feature representations directly from massive datasets. Deep learning (DL) is a subset of machine learning that utilizes artificial neural networks with multiple hidden layers (the 'deep' component) to model high-level data abstractions. This architecture allows the model to perform automatic feature extraction: it learns the optimal features (e.g., edges, then shapes, then objects) instead of requiring manual engineering. DL models power state-of-the-art AI across industries: Convolutional Neural Networks (CNNs) drive image recognition with near-human accuracy, while Transformer models are the foundation for Generative AI like large language models (LLMs) and chatbots (e.g., ChatGPT). - Official technology site: https://www.ibm.com/topics/deep-learning - Public AI Tinkerers demos and talks: 10 - Result page: 1 of 1 ## Recent Public Talks and Demos ### [Tinkerers / AI Growing WorldWide](https://santiago.aitinkerers.org/talks/rsvp_9zUr5V-XrvQ) La mision y experiencia en IA de AI-Tinkerers por el mundo. +200 cuidades del mundo. Una en Chile. - Event context: 🔥 AI TINKERERS - SANTIAGO / CAPĂŤTULO #10 / 2026 — 2026-04-16 — Santiago - Public talk page: https://santiago.aitinkerers.org/talks/rsvp_9zUr5V-XrvQ ### [Exercise Posture Tracking Using Computer Vision](https://raleigh.aitinkerers.org/talks/rsvp_QACg5zj28MM) I've created an unsupervised computer vision algorithm to detect back rounding during deadlift. In short, I've demonstrated that I can measure the curvature of the back in an unsupervised way, and that a rounded back produces a different signal from a flat back. I've used off-the-shelf pose tracking and segmentation models to measure the back curvature. This could help guide learning of proper form during deadlift and other exercises. - Event context: AI Tinkerers Raleigh Meetup — February 11, 2026 — 2026-02-11 — Raleigh - Public talk page: https://raleigh.aitinkerers.org/talks/rsvp_QACg5zj28MM ### [Hexagone: Auto Anonymize for AI](https://paris.aitinkerers.org/talks/rsvp_NKaulXDzh9Q) The session demonstrates Hexagone AI’s tool that automatically redacts personal data in text, images, and audio within PDFs, preserving utility for AI training. - Event context: Epic Demo night 0.2.1 - Sep 18th, 2025 — 2025-09-18 — Paris - Public talk page: https://paris.aitinkerers.org/talks/rsvp_NKaulXDzh9Q ### [Deep Learning Examples](https://orange-county.aitinkerers.org/talks/rsvp_zDJB5B5M_Zc) I am teaching a graduate level deep learning course at LMU. My talk will have some code showing how Deep Learning code can be used to solve real business problems. - Event context: AI Tinkerers - Orange County Inaugural Meetup (February 20th 2025) — 2025-02-21 — Orange County - Public talk page: https://orange-county.aitinkerers.org/talks/rsvp_zDJB5B5M_Zc ### [EEG Signal Classification](https://mexico-city.aitinkerers.org/talks/rsvp_RnOVvzF4rtY) Project to Classify EEG Signals using Deep Learning - Event context: AI Tinkerers and Agents: Mexico City — 2025-02-08 — Mexico City - Public talk page: https://mexico-city.aitinkerers.org/talks/rsvp_RnOVvzF4rtY ### [Large Scale AI/ML Inference with GPUs](https://boston.aitinkerers.org/talks/rsvp_0FT2VStA-DM) Most production AI models using deep learning will be performing computational forward passes for inference. This demo will go into the steps you would take to accelerate and optimize inference on models like LLMs with the power of GPUs and batch processing. We may also be showing the results via a vector database. - Event context: August 2024 Meetup at Æthos — 2024-08-26 — Boston - Public talk page: https://boston.aitinkerers.org/talks/rsvp_0FT2VStA-DM ### [Identifying semiconductor manufacturer from images with convolutional nets](https://boston.aitinkerers.org/talks/rsvp_lI3z3DPEilI) I'd like to showcase simple approaches to use convolutional nets — a fundamental building block for deep learning in computer vision — to identify the manufacturer of a semiconductor chip. Semiconductors looks fairly similar when photographed, and telling them apart can be difficult from just an image. I recently implemented a simple approach to fine-tune pre-trained convnets that solves the task quite well. I'd like to briefly go over the approach I used, how I set up experimentation etc. - Event context: July 2024 Meetup at C10 Labs — 2024-07-22 — Boston - Public talk page: https://boston.aitinkerers.org/talks/rsvp_lI3z3DPEilI ### [Chess Predict](https://la.aitinkerers.org/talks/rsvp_JloIyZD7310) Chess Predict is a software the predicts the best move given a digital screenshot of a chess game. Improvements to the software include an additional smart contract feature which combats cheating. I will demo this functionality. - Event context: June 25th - LA AI Tinkerers Meetup & Demos — 2024-06-26 — Los Angeles - Public talk page: https://la.aitinkerers.org/talks/rsvp_JloIyZD7310 ### [Face Morphing through DL and Convex Combinations](https://medellin.aitinkerers.org/talks/rsvp_q-z_07EzYPo) DemostrarĂ© un pipeline para hacer morphing entre dos imágenes de entrada que contienen rostos.. Este pipeline consta de dos pasos: 1. DetecciĂłn de rostros y de puntos de referencia en rostros usando dos modelos de Deep learning disponibles libremente. 2. GeneraciĂłn de una nueva imagen como una interpolaciĂłn convexa entre las dos imágenes de entrada, informalmente "alpha * Img1 + (1-alpha) * Img2". Cada pixel de esta imagen se calcula a partir de una combinaciĂłn convexa de pixeles correspondientes las imagenes de entrada. - Event context: AI Tinkerers MedellĂ­n #2 - 23 de Mayo — 2024-05-23 — MedellĂ­n - Public talk page: https://medellin.aitinkerers.org/talks/rsvp_q-z_07EzYPo ### [Chess Predict](https://la.aitinkerers.org/talks/rsvp_9IK7niF2MR4) Introduction The Chess Companion App aims to serve chess enthusiasts who aspire to improve their game, challenge higher-level players, and explore various strategic options in their gameplay. This app is particularly tailored for players who may not yet possess the technical prowess to compete with more skilled opponents, such as their "Russian grandmaster dads," but are eager to enhance their abilities and enjoy a more competitive experience. Purpose The app's primary goal is to assist players in making difficult decisions during a game, providing guidance on optimal moves without resorting to outright cheating. It's designed to be a learning tool rather than a means to artificially inflate ELO ratings or achieve unethical victories. Target Audience Chess players seeking to improve their skills. Individuals wanting to challenge higher-level players, including family members with advanced chess knowledge. Players looking for assistance in exploring different move paths and strategies. Beginners or lower-level players desiring to offer a substantial challenge to more experienced friends. Key Features Screenshot Upload Functionality: Users can upload a screenshot of their current chess game. Move Analysis and Suggestions: The app analyzes the uploaded image, identifies the current state of the game, and suggests the best possible move. Difficulty Adjustment: Users can adjust the level of advice, from beginner to advanced, tailoring the suggestions to their learning curve. Move Explanation Feature: Beyond just suggesting a move, the app explains why this move is considered optimal, enhancing the learning experience. Historical Game Analysis: Users can input famous historical games or their past games to see different strategies they could have employed. Ethical Use Reminder: The app includes a disclaimer and guidance on ethical use, emphasizing that it's a tool for learning and self-improvement, not for cheating. - Event context: March 19th - LA AI Tinkerers Meetup & Demos — 2024-03-20 — Los Angeles - Public talk page: https://la.aitinkerers.org/talks/rsvp_9IK7niF2MR4 ## Related Technologies - [BERT](https://aitinkerers.org/technologies/bert) ([Markdown](https://aitinkerers.org/technologies/bert.md)) — 179 public demos - [GPT-3](https://aitinkerers.org/technologies/gpt-3) ([Markdown](https://aitinkerers.org/technologies/gpt-3.md)) — 191 public demos - [GPT-4](https://aitinkerers.org/technologies/gpt-4) ([Markdown](https://aitinkerers.org/technologies/gpt-4.md)) — 529 public demos - [Keras](https://aitinkerers.org/technologies/keras) ([Markdown](https://aitinkerers.org/technologies/keras.md)) — 74 public demos - [LLM](https://aitinkerers.org/technologies/llm) ([Markdown](https://aitinkerers.org/technologies/llm.md)) — 123 public demos - [PyTorch](https://aitinkerers.org/technologies/pytorch) ([Markdown](https://aitinkerers.org/technologies/pytorch.md)) — 273 public demos - [AI](https://aitinkerers.org/technologies/ai) ([Markdown](https://aitinkerers.org/technologies/ai.md)) — 55 public demos - [Artificial Intelligence](https://aitinkerers.org/technologies/artificial-intelligence) ([Markdown](https://aitinkerers.org/technologies/artificial-intelligence.md)) — 5 public demos - [Autoencoder](https://aitinkerers.org/technologies/autoencoder) ([Markdown](https://aitinkerers.org/technologies/autoencoder.md)) — 1 public demo - [BLOOM](https://aitinkerers.org/technologies/bloom) ([Markdown](https://aitinkerers.org/technologies/bloom.md)) — 115 public demos - [ChatGPT](https://aitinkerers.org/technologies/chatgpt) ([Markdown](https://aitinkerers.org/technologies/chatgpt.md)) — 83 public demos - [Chess Predict](https://aitinkerers.org/technologies/chess-predict) ([Markdown](https://aitinkerers.org/technologies/chess-predict.md)) — 1 public demo - [Computer Vision](https://aitinkerers.org/technologies/computer-vision) ([Markdown](https://aitinkerers.org/technologies/computer-vision.md)) — 22 public demos - [Detectron2](https://aitinkerers.org/technologies/detectron2) ([Markdown](https://aitinkerers.org/technologies/detectron2.md)) — 1 public demo - [EEG](https://aitinkerers.org/technologies/eeg) ([Markdown](https://aitinkerers.org/technologies/eeg.md)) — 1 public demo - [Face Detection](https://aitinkerers.org/technologies/face-detection) ([Markdown](https://aitinkerers.org/technologies/face-detection.md)) — 1 public demo - [Gaussian Mixture Model](https://aitinkerers.org/technologies/gaussian-mixture-model) ([Markdown](https://aitinkerers.org/technologies/gaussian-mixture-model.md)) — 1 public demo - [GPUs](https://aitinkerers.org/technologies/gpus) ([Markdown](https://aitinkerers.org/technologies/gpus.md)) — 5 public demos