Introduction to Transfer Learning in Machine Learning with Python

Transfer learning is an exciting and dynamic methodology in the field of machine learning and it’s become a crucial part of any machine learning engineer’s portfolio. It allows models to be recycled so that the knowledge gained from one task can be used for another.

Have you ever wondered how your knowledge of cycling can help you in learning how to drive a car? Or how a chess player learns to play another board game? This is called transfer learning, a concept not exclusive to humans alone but profoundly useful in machine learning too.

In this article, we will take a deep dive into the realm of transfer learning, its types, how it’s implemented in Python, and examine some practical use-case examples. So let’s fasten our belts and prepare for an introspective journey.

What is Transfer Learning?

To put it simply, in transfer learning, a pre-trained model (model trained on a large benchmark dataset) is used as a starting point for a related problem. Generally, deep learning models learn a lot of features in the process of training. Through transfer learning, these features can be leveraged for another model, saving considerable time and computational resources.

Why Use Transfer Learning?

Saves Training Time: It speeds up the rate of machine learning model development by leveraging what a model has already learned.
Lowers Computational Requirement: It minimizes the computational resources needed as we don’t have to train models from scratch.
Better Initial Models: Transfer learning can provide a boost in initial performance and achieve better accuracy.

Types of Transfer Learning

There are broadly two types of transfer learning in the context of machine learning – inductive transfer learning and transductive transfer learning.

Inductive Transfer Learning: It works on the understanding that the learning done on the source task can help improve the learning on the target task.
Transductive Transfer Learning: This learning is concerned with the application of information from specific source task instances to specific target task instances.

Implementing Transfer Learning in Python

We will utilize the Keras library and TensorFlow backend to illustrate how we can use transfer learning for image classification using the MobileNet model.

# Import the necessary libraries
from keras.applications import MobileNet
from keras.layers import Dense,GlobalAveragePooling2D
from keras.models import Model

# Prepare MobileNet model
base_model=MobileNet(weights='imagenet',include_top=False) 

# define the top layers to be added
x=base_model.output
x=GlobalAveragePooling2D()(x)
x=Dense(1024,activation='relu')(x) 
preds=Dense(101,activation='softmax')(x) 

# mentioning the input and output for the model
model=Model(inputs=base_model.input,outputs=preds) 

# Set the first 20 layers of the network to be non-trainable
for layer in model.layers[:20]:
    layer.trainable=False
for layer in model.layers[20:]:
    layer.trainable=True

# Compile the Model
model.compile(optimizer='Adam',loss='categorical_crossentropy',metrics=['accuracy']) 

# Train the Model
model.fit_generator(generator=train_generator,  
                    steps_per_epoch=step_size_train,
                    epochs=5)

The above code essentially takes a pre trained model (MobileNet), tweaks the last few layers, and trains it on a new dataset. The trained model can now classify image instances from the new dataset.

Practical Examples of Transfer Learning

Let’s take a look at some real-life examples where Transfer Learning shines.

1. Self-Driving Cars
Companies like Uber and Waymo use transfer learning using previously collected data on routes, obstacles, and inefficiencies. The model trained on this data can then be applied to different geographies with minimal adjustments.

2. Medical Imaging
Transfer learning is used in radiology to improve cancer detection. Models are trained on a large dataset of cancerous and non-cancerous images and then fine-tuned on smaller, more specific datasets.

In conclusion, Transfer Learning is a game-changer, opening new horizons by saving time, computation expenses and providing impressive results, even with less data. It’s a fantastic tool that every machine learning practitioner should have in their toolbox.

I hope this article gave you a comprehensive understanding of what Transfer Learning is, how it’s implemented in Python, and its practical applications. Happy learning!

Introduction To Transfer Learning In Machine Learning