Deep Learning Foundations
Build the engineering foundations for training and evaluating networks: architectures, optimization, generalization and experiment design.
Explore deep learning
ARTIFICIALGATE · TAILORED ENGINEERING TRAINING
A learning and simulation technology company with an applied R&D arm. We build training for engineering teams and academic institutions around model training, adaptation, experiment design and system integration.
Plan training for your teamBuild the engineering foundations for training and evaluating networks: architectures, optimization, generalization and experiment design.
Explore deep learning
Develop an end-to-end workflow for classification, detection, segmentation, evaluation and integration into an engineering system.
Explore computer vision
Connect perception, action and feedback. Practice physical interfaces, failure diagnosis and demonstration data with interactive laboratories.
Explore Physical AIAdapt the syllabus, difficulty, training hours, case studies and capstone project. Organization-specific data can be incorporated after agreeing on privacy and access requirements.
Instructor-led or blended programs can combine video lessons, books, Google Colab notebooks, simulators, knowledge quizzes, a capstone project and professional guidance. The mix and completion criteria are agreed for each program.
Identify the learners, prerequisites and engineering problem the team needs to solve.
Run experiments, work with code and measurements, compare against a baseline and investigate failures.
Evaluate agreed deliverables and criteria. A completion certificate requires meeting the program requirements.
Founder of ArtificialGate. Academic director of the AI model development and deep learning track at Google-Reichman Tech School; lecturer at the Technion and Reichman University. Holds three Technion degrees, five patents and has authored dozens of AI papers.
Research and publications · Our books