• Pooja Kushwah
• Monika Bhatnagar

Transfer learning is the reuse of a pre-trained model for a new problem, it is very popular nowadays in deep learning because it can train deep neural networks with relatively little data, and it is very useful in data science because of most real problems., you don't have millions of data points marked to train these complex models. Let's take a look at what transfer learning is, how it works, why and when to use it. Includes several resources for models that have been previously trained in learning transfers for example, when you train the classifier to predict whether an image contains food, you can use the knowledge gained during training to recognize drinks, for example, if you trained a simple classifier to predict, if the image includes a backpack, you can use the knowledge gained by the model during training.

"Literature Review of Multiclass Image Classification Based on Inception-v3 Transfer Learning Model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 1, page no.d356-d360, January-2023, Available :http://www.jetir.org/papers/JETIR2301342.pdf

"Literature Review of Multiclass Image Classification Based on Inception-v3 Transfer Learning Model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 1, page no. ppd356-d360, January-2023, Available at : http://www.jetir.org/papers/JETIR2301342.pdf