Deep learning in the IoT context

Supervised Learning Overview

Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.

Reinforcement Learning Concepts

Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.

Mathematics of Data: Deep Learning Introduction

Covers a mathematical introduction to deep learning, including challenges, power of linear classifiers, model scaling, and theoretical aspects.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Media Security Standards

Explores the definition of media security standards and the standardization of a framework rather than a specific security tool.