What Is Transfer Learning? | Transfer Learning in Deep Learning | Deep Learning Tutorial|Simplilearn
Summary
TLDRThis video introduces transfer learning, a powerful technique in machine learning where a pre-trained model is used to improve predictions on new tasks. It covers the benefits, such as reduced training time and the ability to work with limited data, and explains how transfer learning works, especially in areas like computer vision and natural language processing. The video also highlights popular pre-trained models like VGG16, InceptionV3, BERT, and GPT. Viewers are encouraged to explore more through certification programs in AI, machine learning, and other cutting-edge domains to advance their careers.
Takeaways
- 📚 Transfer learning allows using a pre-trained model to improve predictions on a new, related task.
- ⚙️ It is commonly used in fields like computer vision and natural language processing (NLP).
- 🚀 Transfer learning reduces training time and improves performance, even with limited data.
- 🔄 Retraining only the later layers of a neural network can tailor it for a new task, while keeping early layers intact.
- ⏱️ Using pre-trained models saves time compared to training from scratch, especially for deep learning models.
- 🔍 Feature extraction identifies important patterns in data at different neural network layers.
- 📊 Popular models used in transfer learning include VGG16, Inception V3 for image recognition, and BERT, GPT for NLP.
- 💻 Transfer learning helps leverage large datasets like ImageNet or extensive text collections for new tasks.
- 🎓 Simply Learn offers an AI and ML program in collaboration with IBM and Purdue University for upskilling.
- 🎯 The program covers topics like machine learning, deep learning, NLP, computer vision, and more.
Q & A
What is transfer learning in machine learning?
-Transfer learning is a technique in machine learning where a pre-trained model, which has been trained on a related task, is reused to improve predictions on a new task. It leverages knowledge gained from a previous assignment to solve similar problems more efficiently.
How does transfer learning work in computer vision?
-In computer vision, neural networks detect edges in the early layers, identify forms in the middle layers, and capture task-specific features in the later layers. Transfer learning reuses the early and middle layers of a pre-trained model and retrains the later layers for a new, related task.
What are some benefits of using transfer learning?
-Transfer learning reduces training time, improves neural network performance, and allows models to work well with limited data. This is especially helpful when large datasets are unavailable, as the pre-trained model has already learned general features.
Why is transfer learning useful for tasks like NLP?
-Transfer learning is beneficial for NLP tasks because obtaining large labeled datasets in this domain can be challenging. Pre-trained models, which have been trained on vast amounts of text, allow efficient use of limited data and reduce training time.
What are the steps involved in using transfer learning?
-The steps include: 1) Training a model to reuse for a related task, 2) Using a pre-trained model, 3) Extracting features from the data, and 4) Refining the pre-trained model to suit the specific new task.
What is the role of feature extraction in transfer learning?
-Feature extraction involves identifying meaningful patterns in the input data. In neural networks, the early layers extract basic features like edges, while deeper layers capture more complex patterns. These extracted features are used to improve predictions.
Which popular models have been trained using transfer learning?
-Some popular models include VGG16 and VGG19 (trained on ImageNet for image classification), InceptionV3 (known for object detection), BERT (used in NLP tasks like sentiment analysis), and the GPT series (used in a wide range of NLP tasks).
How does transfer learning speed up the training process?
-By using a pre-trained model, the network has already learned general features, so the training starts from an advanced point. This reduces the need for extensive training time, which is especially beneficial for complex tasks that would otherwise take days or weeks.
What is the difference between training a model from scratch and using a pre-trained model?
-Training a model from scratch requires feeding raw data and requires extensive computation and time. Using a pre-trained model allows you to build on knowledge it already gained, requiring less data and computation, while achieving faster and more accurate results.
Why is transfer learning effective in domains like computer vision and NLP?
-Transfer learning is effective in these domains because the models can reuse fundamental features such as edges in images or sentence structures in text. This allows them to adapt to new tasks more quickly and with less data, while still performing effectively.
Outlines
📚 Introduction to Transfer Learning and Course Overview
The paragraph introduces the concept of training classifiers and how it can predict cuisine types. It also sets the context for the video, which will cover transfer learning, including what it is, how it works, and why it is important. The video also promises to discuss the steps involved and showcase popular models trained using transfer learning. Additionally, viewers are encouraged to subscribe to stay updated on technology trends. Towards the end, the paragraph promotes a postgraduate program in AI and machine learning, highlighting its collaboration with IBM and prestigious certifications.
🤖 What is Transfer Learning and How It Works
This section defines transfer learning as the use of a pre-trained model to improve predictions for a new task. It explains that the method allows for knowledge gained in one task to be applied to a related task, saving computational resources. For example, a model trained to recognize backpacks can be retrained to identify sunglasses. The neural networks' different layers detect distinct features, and transfer learning focuses on reusing early layers while retraining the later layers. This approach is especially useful for tasks like computer vision and natural language processing.
Mindmap
Keywords
💡Transfer Learning
💡Pre-trained Model
💡Feature Extraction
💡Neural Networks
💡VGG16 and VGG19
💡Inception V3
💡BERT (Bidirectional Encoder Representations from Transformers)
💡GPT (Generative Pre-trained Transformer)
💡Faster Training
💡Limited Data
Highlights
Introduction to transfer learning and its importance in machine learning.
Training classifiers to recognize different objects, such as beverages and backpacks.
Transfer learning involves using knowledge from a pre-trained model to improve predictions on new tasks.
Transfer learning is highly useful in computer vision and natural language processing tasks like sentiment analysis.
How neural networks function, with earlier layers detecting basic patterns and deeper layers identifying task-specific features.
Transfer learning reduces training time and enhances model performance, especially when working with limited data.
Using a pre-trained model is beneficial as it avoids the need for training a model from scratch.
Feature extraction in neural networks involves capturing meaningful patterns at different layers.
Efficient data usage with pre-trained models allows for good performance with limited labeled datasets.
Transfer learning leads to faster training times by leveraging existing general features from a related problem.
Popular transfer learning models include VGG16, Inception V3, and BERT for different tasks in image and text processing.
VGG models were trained on the ImageNet dataset for image classification, while Inception V3 excels at object recognition.
BERT and GPT are widely used for tasks such as sentiment analysis, name entity recognition, and language modeling.
The process of re-training specific layers in transfer learning helps fine-tune models for new tasks.
Transfer learning is an essential technique for AI and machine learning professionals to save time and computational resources.
Transcripts
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do you know training a classifier to
distinguish beverages can help predict
the cuisine of an image to know more
about cancer learning and how it works
stay tuned till the end of this video
in this video we will cover topics like
what transfer learning is how transfer
Learning Works moving forward we will
dive into why you should use transfer
learning after that we will cover the
steps to use transfer learning and at
the end we will see popular model
trained using transfer learning let me
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the description box below so without any
further Ado let's get started so what is
transfer learning transfer learning is
machine learning refers to G using a
pretend model to improve prediction on a
new task it involves using knowledge
gained from a previous assignment to
tackle a related problem for instance a
model trained to recognize backpacks can
also be used to identify other objects
like sunglasses due to the substantial
CPU power required this approach is
widely utilized income Division and
natural language processing tasks
including sentiment analysis so moving
forward let's see how transfer Learning
Works so how does transfer learning work
in computer vision neural network have
distinct objectives for each layer
detecting edges in the first layer and
identifying forms in the middle layer
and capturing tasks specific features in
the later layer transfer learning
utilize the early and Center layers for
a pre-pretent model and only retrains
the later layer it leverages the label
data from its original task for instance
if you have a model trained to identify
backpacks in images and now want to use
it to detect sunglasses we will retrain
the later layers to understand the
distinguished features of sunglasses
from the other objects so moving forward
let's see why should you use transfer
learning transfer learning offers
several advantages including reduced
training time improved neural network
performance in most cases and the
ability to work with limited data
training a neural model from scratch
typically requires a substantial amount
of data which may not always be readily
available transfer learning becomes
valuable in such scenario here is why
you should consider using transfer
learning first one is efficient use of
data with pre-trained models you can
perform well even with limited training
data that is specially beneficial in
tasks like NLP where obtaining large
label data set can be challenging and
time Computing the second one is faster
training building a deep neural network
from a scratch of a complex task can be
time consuming taking days or even weeks
by leveraging transfer learning the
training time is significantly reduced
as you start with a model that has
already learned General features from a
related problem now moving forward let's
see steps to use transfer learning the
first one is training a model to reuse
it in machine learning training a model
involves providing it with the data to
learn patterns and make prediction once
a model is trained on a specific task it
can be reused and repurpose for related
tasks saving time and computational
resources the second one is using a
pre-trained Model A pre-trained model is
a model that has already been trained on
a larger set for a specific task instead
of training a model from scratch using a
pre-trained model as a starting point
allow us to benefit from the knowledge
it has gained during its previous
training the third one is extraction of
features feature extraction is a process
in which meaningful patterns and
characteristics are identified and
separated from a raw data in the context
of machine learning it involves
identifying relevant information from
input data to feed into a model for a
better prediction the fourth one is
extraction of features in neural
networks a neural networks feature
extraction involves identifying
important patterns or features in the
data at different network layers the
early layers typically capture simple
features like edges while deeper layers
capture more complex feature relevant to
the task at hand this hierarchical
representation enables neural network to
learn and generalize from the data
effectively
so moving forward let's see some popular
models trained using transfer learning
so numerous machine learning models have
been trained using transfer learning
some popular ones include for the first
one is
vgg16 and vgg 90.
these models were trained on the image
net data set for image classifications
test the second one is inception V3
these models were pre-trained on
imagenet and are known for their
effectiveness in which rapid in object
detect repeat in object detection and
object recognition the third one is bird
bi-directional encoder representation
from Transformer this language model is
written on the extensive text collection
and find extensive application in NLP
tasks like sentimental analysis and name
entity recognition the fourth one is GPT
generative pre-trained Transformer
series these models are printed language
models for various NLP tasks
these are just a few example of
pre-trained models that have been used
in transfer learning to accelerate
training and improve performance across
different tasks and with that we have
come to end of this video on what is
transfer learning I hope you found it
useful and entertaining please ask any
question about the topics covered in
this video in the comments box below our
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