These are assignments I completed while going through a Deep Learning Specialization course on Coursera offered by deeplearning.ai by Andrew Ng. These files are supposed to be here for my own reference, please refrain from copying. I would not be responsibel for your loss, if any.
The course structure is:
Regular plain deep neural networks, contains:
- Logistic Regression as a Neural Network
- Planar data classification with one layer NN
- Handwritten digit recognition and classification
- Other image recognition
Hyperparameter tuning and training efficiency, intro to Tensorflow. Contains:
- Gradient Checking
- Initialization
- Regularization
- Optimization Methods
- Tensorflow NNs and intro
Structuring machine learning projects (no code, hence no file uploaded).
Convolutional neural nets, Tensorflow and Keras. Conatins:
- Convolutional models
- Keras intro and simple models
- Resnets
- Car Detection in Images (YOLO v2 algorithm)
- Face Recognition
- Neural Style transfer