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
Join our public courses in our Canada facilities. Private class trainings will be organized at the location of your preference, according to your schedule.