Topics to learn in Neural Network I have recently started learning Deep Neural Networks and was going through the tutorials online. Everywhere I saw that the topics post Image classification using CNN is a little hazy. No one seems to follow a guide as to what topics should one learn after learning image classification.
I would really appreciate if someone could guide me so as to learn advanced topics and become good to get started with research in this field.
The topics i have covered so far are:

Basic Machine Learning

Linear Regression
Logistic Regression
SVM
Decision Trees
Optimization Algorithms
Regularization Methods
Different Loss Functions
Different Metrics during Training
Feature Extraction
HyperParameter Tuning


Basic Neural Network

ANN
CNN

Image Classification
Image Augmentation before training
Visualizing Filters





I have started learning Object detection using CNN but I have this feeling that I am missing some topics which can strengthen my foundation.
Thanks in advance.
Any guidance is appreciated.
If you want to start with research, as you say, I would make sure not to skip the theory and underlying mathematics of machine learning. Knowing what model and hyperparameters to choose for a certain problem and being able to implement this quickly is very useful in corporate settings, but research mostly requires a deep mathematical understanding of what is going on.
In my opinion the book 'deep learning' by Goodfellow and Bengio has a good structure that allows you to get familiar with the mathematical foundations of deep learning. When you understand this, you will be able to read and understand state-of-the art papers if you want and do your own research. You can find the book for free online I think.