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Here you find a collection of articles on deep learning.
How to Learn Deep Learning
Deep Learning Foundations
After reading and understanding these posts you should have a basic understanding of neural networks.
- How Do Neural Networks Learn (Forward Propagation)
- Understanding Backpropagation With Gradient Descent
- Neural Network Layers and Architecture
- An Introduction to Neural Network Loss Functions
- Feature Scaling and Data Normalization for Deep Learning
- Weight Decay in Neural Networks
- Dropout Regularization in Neural Networks
- Understanding The Exploding and Vanishing Gradients Problem
- Stochastic Gradient Descent versus Mini Batch Gradient Descent versus Batch Gradient Descent
- Deep Learning Optimization Techniques for Gradient Descent Convergence
- What is Batch Normalization And How Does it Work?
- Building a Neural Network in TensorFlow: Step By Step Example
Deep Learning For Computer Vision
- What is a Convolution: Introducing the Convolution Operation Step by Step
- Understanding Convolutional Filters and Convolutional Kernels
- Understanding Padding and Stride in Convolutional Neural Networks
- What is Pooling: Pooling Layers Explained
- Convolutional Neural Network Architecture
- Deep Learning Architectures for Image Classification: LeNet vs Alexnet vs VGG
- ResNets and Skip Connections
- Foundations of Deep Learning for Object Detection
- Deep Learning Architectures for Object Detection: YOLO vs. SSD vs. RCNN
Applied Deep Learning for Computer Vision
- Building a Convolutional Neural Network for Image Classification: A Step-by-Step Example in TensorFlow
- Deep Learning for Semantic Image Segmentation: A Worked Example in TensorFlow
For writing these posts I’ve relied on several textbooks, online courses, and blogs.
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