This course was designed to bring anyone up to speed on Machine
Learning & Deep Learning in the shortest time.
This particular field in computer engineering has gained an exponential growth in interest worldwide following major progress in this field.
The course starts with building on foundation concepts relating to Neural Networks. Then the course goes over Tensorflow libraries and Python language to get the students ready to build practical projects.
The course will go through four types of neural networks:
1. The simple feedforward
4. Generative Adversarial
You will build a practical Tensorflow project for each of the above Neural Networks. You will be shown exactly how to write the codes for the models, train and evaluate them.
Here is a list of projects the students will implement:
1. Build a Simple Feedforward Network for MNIST dataset, a dataset of handwritten digits
2. Build a Convolutional Network to classify Fashion items, from the Fashion MNIST dataset
3. Build a Recurrent Network to generate a text similar to Shakespeare text
4. Build a Generative Adversarial Network to generate images similar to MNIST dataset
- Getting Ready - Install Python, Jupyter Notebook and Tensorflow
- Components of Deep Neural Networks
- Training Neural Networks
- Tensorflow Libraries for Deep Learnig
- Convolution Neural Networks
- Recurrent Neural Networks
- Generative Adversarial Networks