Deep Learning(DL) have got some serious traction in the past couple of years and many of us are fascinated by these technologies and want to start learning working in these…

Most of us are familiar with what DL is, so I won’t cover that in this article, rather I’d like to focus on how to start a DL project.

Research, research and more research :

Bet you thought I’d start with data pre-processing, but NO… And there’s a good reason for it. Before you start any project, you need to research about the project you’re about to undertake, the technologies and domain knowledge that is required for the same. DL projects revolve all around data, and proper dataset and it’s processing is the key to getting good results.

Points to consider while researching :

1. Form of data_ :_
2. Identifying the form of data (csv, image, audio, text, etc.) is very important as the pre-processing to be done on the data may require a lot of domain knowledge.
3. For example : a typical csv dataset may be easily worked on, but for say audio data, one needs to have domain knowledge to select and process attributes from the audio to get best results
4. Type of Network:
5. Great, now that you’ve identified the form of data, it’s time to select an approach. In DL, one can select different type of networks amongst Artificial Neural Network(ANN), Convolutional Neural Network(CNN), Long-term Short-term Memory Networks(LSTM), etc. Reading research papers (Google Scholar_ is a great way to search) and approaches other developers have used will help to decide an approach of your own. Study the approach you select and different forms of it._
6. Tools, libraries and environment:
7. Select a deep learning library you’re familiar with (Tensorflow, PyTorch, Keras, etc.) and according to mode of deployment of your model. Personally, I love the simplicity of Keras and I’d recommend the same for beginners.
8. _DL is computationally expensive, so if hardware is an issue, Google Colab is a great option where you can code your project from any device, and it runs on powerful hardware provided by Google (And its Free!). Completely online coding environments like __GitPod _are also useful and have Git integration built in with them.

#deep-learning #research #dataset #data-pre-processing #getting-started #deep learning