#75 Machine Learning & Data Science Challenge 75
Greetings.
I am a machine learning engineer based in India, possessing a sustained interest in machine learning since my undergraduate studies. I have completed Stanford University's machine learning course (Andrew Ng) via Coursera, and IBM's machine learning and deep learning curriculum. My current focus is on machine learning and data science projects, aiming to leverage my expertise for impactful, real-world problem-solving.
What is Transfer learning in Deep Learning?
Transfer Learning:
It is a machine learning method where a model is developed for the task and is again used as the starting point for a model on a second task.
It is a popular approach in deep learning where pre-trained models are used as the starting point for computer vision and natural language processing tasks are given the vast computing and time resources required to develop neural network models on these problems.
Transfer learning is a machine learning technique where a model trained on one task is re-purposed on a second related task.
Transfer learning is an optimization that allows rapid progress or improved performance when modeling the second task.
Transfer learning only works in deep learning if the model features learned from the first task are general.
