Introduction to Convolutional Neural Networks with TensorFlow
Jul 1, 2024
Introduction to Convolutional Neural Networks with TensorFlow
Speaker
Neil Leiser: Data Scientist at Iwaka (Fintech startup) and host of AI Stories Podcast.
Event Organizer
Nathan Paccini: Marketing Manager at Data Science Dojo.
Overview
Introduction to convolutional neural networks (CNNs) with TensorFlow.
Brief tutorial included in presentation (referred to as part two).
Theoretical insights and practical guide on CNNs.
About Neil
Background: Born in Belgium, moved to London seven years ago, studied civil engineering at Imperial College.
Shift to Data Science: Post-2019, completed master's in data science in London. Research thesis on predicting solar panel output using satellite images led to deep dive into CNNs.
Current Role: Builds machine learning algorithms for credit risk at Iwaka.
Podcast: Hosts AI Stories Podcast interviewing data professionals and tech leaders.
Agenda
Brief introduction to AI and neural networks (5 minutes) to align understanding.
Theory behind convolutional neural networks (CNNs) to show the intuition of their operation.
Practical session using Google Colab to build a CNN algorithm with Python and TensorFlow.
Introduction to AI and Neural Networks
AlexNet Paper (2012): Pivotal in popularizing deep learning, set new record in the ImageNet Challenge (image classification); error rate reduced significantly from 26% to 16%.
Historical Context: Neural networks since the 1980s; CNNs since the 1990s.
Key Factors for Success: Availability of computing power and large datasets by 2012.
Basics of Neural Networks:
Composed of neurons (nodes) connected by weights (edges).
Three key parts: Input layer, hidden layers, output layer.