I hope that you would now be able to apply pre-trained models to your problem statements. Be sure that the pre-trained model you have selected has been trained on a similar data set as the one that you wish to use it on. There are various architectures people have tried on different types of data sets and I strongly encourage you to go through these architectures and apply them on your own problem statements. Please feel free to discuss your doubts and concerns in the comments section.
It has always been a debatable topic to choose between R and Python. The Machine Learning world has been divided over the preference of one language over the other. But with the explosion of Deep Learning, the balance shifted towards Python as it had an enormous list of Deep Learning libraries and frameworks which R lacked (till now). I personally switched to Python from R simply because I wanted to dive into the Deep Learning space but with an R, it was almost impossible. With launch of Keras in R, this fight is back at the center.
Keras is a deep learning neural network library written in Python that works on a high level. It is running on top of backend libraries like Tensorflow (or Theano, CNTK, etc.) which is capable of doing calculations on a low level, like multiplying tensors, convolutions and other operations. This library has many pros, like, it is very easy to use once you get familiar with, it allows you to build a model of neural network in a few lines of code. It is highly supported by the community, it can run on top of many backend libraries as we mentioned earlier, can be executed on more than one GPUs and so on. In this example, we are going to install Tensorflow, as it is the most used and the most popular one.
This post is taken from Kite Blog written by Daniel Pyrathon. Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed. Think of how efficiently (or not) Gmail detects spam emails, or how good text-to-speech has become with the rise of Siri, Alexa, and Google Home. Machine Learning is an enormous field, and today we'll be working to analyze just a small subset of it. Supervised learning is one of Machine Learning's subfields.
All machine Learning beginners and enthusiasts need some hands-on experience with Python, especially with creating neural networks. This tutorial aims to equip anyone with zero experience in coding to understand and create an Artificial Neural network in Python, provided you have the basic understanding of how an ANN works. Before dipping your hands in the code jar be aware that we will not be using any specific dataset with the aim to generalize the concept. The codes can be used as templates for creating simple neural networks that can get you started with Machine Learning. We will use the Keras API with Tensorflow or Theano backends for creating our neural network.