Image and Video Processing
- How Digital Cameras Process Images
- How Image Compression Works
- How Video Compression Works
- Computer Vision vs Image Processing
- How Super Resolution Works
Deep Learning Crash Course
- Deep Learning Crash Course: Introduction | Lecture 1
- Artificial Neural Networks Demystified | Lecture 2
- Artificial Neural Networks: Going Deeper | Lecture 3
- Overfitting, Underfitting, and Model Capacity | Lecture 4
- Regularization | Lecture 5
- Data Collection and Preprocessing | Lecture 6
- Convolutional Neural Networks Explained | Lecture 7
- How to Design a Convolutional Neural Network | Lecture 8
- Transfer Learning | Lecture 9
- Optimization Tricks: momentum, batch-norm, and more | Lecture 10
- Recurrent Neural Networks | Lecture 11
- Deep Unsupervised Learning | Lecture 12
- Generative Adversarial Networks | Lecture 13
- Practical Methodology in Deep Learning | Lecture 14
- Network Architecture Search: AutoML and others
- How to Design a Neural Network | 2020 Edition
- Fast and Efficient Training of Neural Networks (feat. CodeEmporium)
Hands-on Deep Learning: TensorFlow Coding Sessions
Hands-on Deep Learning: TensorFlow Coding Sessions
- TensorFlow Coding Session #1 Introduction
- TensorFlow Coding Session #2 Training a Model
- TensorFlow Coding Session #3 Training and Validation Sets
- TensorFlow Coding Session #4 Regularization, Checkpoints, and TensorBoard
- TensorFlow Coding Session #5 Convolutional Neural Networks
- TensorFlow Coding Session #6 TFRecords and Freezing Models
- TensorFlow Coding Session #7 Transfer Learning
- What is new in TensorFlow 2.0 | Tutorial