Advanced Deep Learning Specialization Training in New Zealand

In this training, participants will specialize in Deep Learning and learn how to build their own Neural Network from scratch. Thanks to the training content prepared with practical real-life examples, at the end of the training, participants will not only have a theoretical expertise in Deep Learning, but also have a solid infrastructure in practice. 

After that we will continue to explore computer vision and recommendation system tasks using deep learning. This course combines both theory and practice to give students deep understanding of the subject and hands-on experience via coding.

Participants must have experience in coding in Python language. If they don't, taking Python Programming course before this is recommended. 

- Those who want to continue to improve themselves in this field after attending the Deep Learning Introductory Training

- Those who want to specialize in Deep Learning

- Developers familiar with the Python programming language who want to learn how to implement a Deep Learning solution in real life

PyTorch

Advanced Deep Learning

Convolutional Neural Network (CNN)

Recommendation System with Deep Learning

Introduction to PyTorch

Pytorch Tensors

Broadcastin

Reshaping

Concatenation-Stacking

Automatic Differentiation

(Other more advanced Pytorch functionalities will be shown in the deep learning section as we create our own neural network)

Foundation of Deep Learning

What should we focus when we first start the project ?

Baseline Model

Why is Gradient the direction of the greatest increase ?

Foundation of Neural Network -Everything can be thought of as functions

Why to use sigmoid, really ?

Adding Non-linearity

Why normalizing help with training ? - Same space

Understanding Regularization

Defining Loss function

Loss function vs Metric - Loss is for machine Metric is for you

What is batch and why its size is important ?

Binary Classification from scratch

Multi-class classification from scratch

Residual Block

Batch Normalization

Convolutional Neural Network (CNN)

Filtering inputs and their effects on outcome

Making filters learnable

Convolutions as feature extractors

Recommendation System with Deep Learning

What is entity embeddings

How to build deeper networks

Visualization of biases and weights of network