Deep Learning and Computer Vision Course 2022
- Iris problem (Dense, classification)
- Fashion-MNIST problem (Dense, classification)
- CIFAR-10 problem (CNN, classification)
- Fashion-MNIST problem (CNN, classification)
- Handwritten Digits Recognition problem (CNN, classification)
- Learning Rate Finder
- Write Coustom CallBack
- Using pretrainded model
- Flowers problem using tf.data (CNN, classification)
- Cat vs Dog problem using tf.data (CNN, classification)
Introduction:
- What is artificial intelligence?
- The difference between AI, deep learning and machine learning
- General and weak artificial intelligence
- Unsupervised and supervised algorithms
Neural networks:
- Perceptrons and neurons
- Activation function
- Cost and error function
- Multilayer neural network
- Neural network optimization
- Implementation in Tensorflow / Keras framework
Deep neural networks:
- Convolutional neural networks
- Transfer learning
- Dropout technique
- Data redundancy
- Batch Normalization
Image classification with convolutional neural networks:
- Classification of digits
- 2013 Cat / Dog Challenge Classification
Model definition methods:
- Sequential
- Functional
- Model Subclassing
Generative Adversarial Networks (GANs):
- Simple GAN
- DCGAN
Other topics:
- Explainable AI and GradCAM
- Tensorboard
- Callbacks