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- 2020.05.11 MDLI ops #1 (Hebrew)
- 2020.06.08 CVPRi #1 lectures (Hebrew)
- 2020.06.13 CVPRi #2 lectures (Hebrew)
- 2020.07.09 ICMLi 2020
- 2020.08.23 ECCVi #1
- 2020.08.23 ECCVi #2
- 2020.08.28 ACLi #1
- 2020.08.31 ACLi #2
- 2020.09.10 MDLI Ops #2 (Hebrew)
- 2020.09.17 Towards fully unsupervised learning - Prof. Trevor Darrell
- 2020.11.29 Applied ML seminars - synthetic data (Hebrew)
- 2020.12.09 ML Advanced Methods [Hebrew]
- 2020.12.22 NeurlPSi #1 2020 {Hebrew}
- 2020.12.27 NeurlPSi #2 2020 {Hebrew}
- 2021.02.17 Applied ML seminars - Anomaly detection (Hebrew)
- 2021.05.08 Risks and Opportunities in the AI World [Hebrew]
- 2021.06.08 CVPRi 2021 #1(Hebrew)
- 2021.06.09 CVPRi 2021 #2 (Hebrew)
- 2021.07.27 ICMLI 2021 #1 (Hebrew)
- 2021.07.27 ML Advanced Methods #2 (Hebrew)
- 2021.07.27 The AutoML conference 2021
- 2021.07.29 ICMLI 2021 #2 (Hebrew)
- 2021.08.02 ACLi 2021 #1 [Hebrew]
- 2021.08.04 ACLi 2021 #2 [Hebrew]
- 2021.08.10 AutoML conference speakers 2021
- 2021.08.22 AutoML in RecSys - Industry Use Cases - Assaf Klein & Hila Weisman-Zohar, Outbrain
- 2021.08.22 AutoML will eat our world - Andraz Tori, Outbrain
- 2021.08.22 Brute force feature engineering - Yam Peleg, Deep Trading
- 2021.08.22 From Sequential to Parallel, a story about Bayesian Hyperparameter Optimization - Andres Asaravicius
- 2021.08.22 How to build scalable and generic ML data pipelines - Nofar Mishraki, Pecan.ai
- 2021.08.22 Latency-Aware NAS with Multi-Objective Bayesian Optimization - Maximilian Balandat, Facebook
- 2021.08.22 NEON Zero-Shot Compression Agent for DenseNetworks - Dr. Gilad Katz, Ben Gurion University
- 2021.08.22 Next Generation AutoML - Noam Brezis, Pecan.ai
- 2021.08.22 Panel AutoML - Are We Ready
- 2021.08.22 The rise of Neural Architecture Search (NAS) and its limitations - Yonatan Geifman, Deci AI
- 2021.12.17 ICCVi 2021 #1 (hebrew)
- 2021.12.17 ICCVi 2021 #2 (Hebrew)
- 2021.12.18 MDLI - Speech Talks (Hebrew)
- 2021.12.18 NeurIPSi 2021 #1
- 2021.12.18 NeurIPSi 2021 #2
- 2022.01.17 MDLI ops 2022 Conference
- 2022.01.25 Are ML Engineers real - Yoav Ramon, Hi Auto
- 2022.01.25 Building a World-Class Supercomputer - Gilad Shainer, Nvidia
- 2022.01.25 Building a modern DS work environment with Kubernetes - Lidiya Norman and Nadav Bar-Uryan, Big Panda
- 2022.01.25 Building an ML Platform from Scratch - Alon Gubkin, Aporia
- 2022.01.25 Developing & Handling a ML algorithm in a non-technical environment - Michael Winer, AppsFlyer
- 2022.01.25 Feature Stores - Unified Data Pipelines for ML - Ran Romano, Qwak
- 2022.01.25 Implementing MLOps best practices with Amazon SageMaker - Gili Nachum, AWS
- 2022.01.25 Inference Workloads Solving Challenges Beyond Training Models
- 2022.01.25 MLOps Beyond Training Simplifying and Automating the Operational Pipeline - Yaron Haviv, Iguazio