SEMESTER SECOND
FINAL YEAR : BE
BRANCH ::COMPUTER / CS
Subject
:Deep Learning(Notes)
Machine Learning:
·
Machine learning is a subset of artificial
intelligence that allows systems to automatically learn and improve from
experience without being explicitly programmed.
·
In other words, it enables computer
programs to learn from data, recognize patterns, and make predictions or
decisions based on the learned patterns.
Machine Learning Model Process:
The machine learning process typically involves
the following steps:
1.Data Collection: The first step in the machine learning process is to
collect relevant data from various sources, including databases, APIs, and
external sources.
2.Data Preprocessing: The collected data needs to be preprocessed to
ensure that it is in a suitable format for machine learning algorithms. This
may involve tasks such as data cleaning, data transformation, and feature
selection.
3.Data Splitting: The preprocessed data is then split into training,
validation, and testing sets. The training set is used to train the machine
learning model, the validation set is used to tune the model's hyperparameters,
and the testing set is used to evaluate the model's performance.
4.Model Selection: The next step is to select a suitable machine learning
model for the task at hand. This may involve choosing between different types
of models, such as decision trees, neural networks, or support vector machines.
5.Model Training: The selected machine learning model is then trained on
the training data set. During training, the model learns from the input data
and adjusts its parameters to improve its performance on the task.
6.Model Evaluation: Once the model is trained, it is evaluated on the
validation set to assess its performance. This step involves measuring various
metrics such as accuracy, precision, and recall.
7.Model Tuning: Based on the evaluation results, the model's
hyperparameters may be adjusted to improve its performance on the validation
set. This process may involve fine-tuning the model's architecture, regularization
techniques, or optimization algorithms.
8.Final Model Evaluation: The final step is to evaluate the model's
performance on the testing set. This step provides an unbiased estimate of the
model's performance on new, unseen data.
9.Model Deployment: If the model performs well on the testing set, it can
be deployed in a production environment to make predictions on new data. This
may involve integrating the model into an application or API that can be used
by end-users.
Advantages of machine learning:
1. Improved
accuracy: Machine learning algorithms can often achieve higher
accuracy than traditional rule-based methods.
2. Automation:
Machine learning can automate complex and repetitive tasks, saving time and
effort for humans.
3. Scalability:
Machine learning algorithms can be applied to large datasets and can be easily
scaled up to handle more data.
4. Personalization:
Machine learning algorithms can learn from user data to provide personalized
recommendations and experiences.
Disadvantages of machine learning:
1. Data
dependency: Machine learning algorithms require large
amounts of data to train effectively, and the quality of the output is highly
dependent on the quality of the input data.
2. Interpretability:
Machine learning models can be difficult to interpret, which can be a challenge
for understanding how decisions are made.
3. Bias:
Machine learning models can be biased towards certain groups or outcomes,
depending on the input data and how the algorithm is designed.
4. Vulnerability:
Machine learning models can be vulnerable to attacks, such as adversarial
attacks, which can cause the model to make incorrect predictions.
Difference Between Machine Learning and
Deep Learning
|
|
Machine Learning |
Deep Learning |
|
Architecture |
Machine learning
models typically have a simpler architecture |
deep learning
models, which are composed of multiple layers of interconnected nodes. |
|
Data size |
Machine learning
models are effective with small to medium-sized datasets |
while deep
learning models require large amounts of data to train effectively |
|
Feature
extraction |
Machine learning
requires humans to manually extract features from data |
deep learning
models can automatically learn features from raw data. |
|
Algorithm
complexity |
Machine learning
algorithms tend to be simpler and more interpretable |
deep learning
algorithms are more complex and less interpretable |
|
Domain expertise |
Machine learning
typically requires domain expertise to identify relevant features and select
appropriate algorithms |
deep learning
can learn relevant features automatically |
|
Training time |
Machine learning
models can be trained quickly |
deep learning
models require a lot of computational power and time to train |
Difference Between Supervised and Unsupervised
Learning
|
Supervised Learning |
Unsupervised
Learning |
|
In supervised
learning, the input data consists of labeled |
In unsupervised
learning, the input data consists of unlabeled |
|
In supervised
learning, the goal is to learn a function that maps input data to output data |
In unsupervised
learning, the goal is to find patterns and structure in the input data. |
|
In supervised
learning, the model receives feedback in the form of labels or error signals
that indicate how well it is performing on the task |
In unsupervised
learning, there is no explicit feedback, and the model must find its own
structure in the input data. |
|
Supervised
learning models require a large amount of labeled data to train effectively |
unsupervised
learning models can often learn from smaller amounts of unlabeled data |
|
Supervised
learning models are often more complex |
Unsupervised
learning models are less complex than supervised learning |
|
In supervised
learning, the model is typically evaluated on a held-out set of labeled data |
unsupervised
learning, the model's performance is often evaluated by how well it can
reproduce or transform the input data |
|
Examples of
supervised learning include image classification, speech recognition, and
language translation. |
Examples of
unsupervised learning include clustering, dimensionality reduction, and
anomaly detection. |
Bias And Variance
Bias and variance are two important concepts in
machine learning that describe the behavior of a model on different datasets.
Bias :
·
Bias refers to the difference between the
expected value of the predictions made by a model and the true values of the
target variable.
·
In other words, bias measures how much the
model's predictions deviate from the true values due to assumptions,
simplifications, or limitations in the modeling process
·
Example: Suppose you want to predict the
heights of children based on their age. If you assume that all children grow at
the same rate and use a linear model that only considers age as a feature, your
model will have high bias and will underestimate the heights of taller children
and overestimate the heights of shorter children.
Variance:
·
Variance refers to the variability of the
model's predictions on different datasets
·
In other words, variance measures how much
the model's predictions change when the training data is perturbed High
variance means that the model is too
complex and can fit the noise in the data instead of the underlying patterns
·
Example: Suppose you want to predict the
performance of a student on a test based on their study hours and sleep hours.
If you use a deep neural network with many layers and parameters, your model
will have high variance and will perform well on the training set but may
generalize poorly to new data. This is because the model is too flexible and
can memorize the training examples instead of learning the true relationship
between the features and the target.
To achieve good performance, you need to find a
balance between bias and variance by selecting an appropriate model complexity,
regularization techniques, and evaluation metrics. This can help you identify
the sweet spot where your model has low bias and low variance and can
generalize well to new data.
Bias Variance Tradeoffs
·
Bias refers to the error that is
introduced by approximating a real-world problem with a simplified model, while
variance refers to the amount that the model output varies as a result of
changing the input data.
·
A model with high bias tends to underfit
the training data, while a model with high variance tends to overfit the
training data
·
To achieve good generalization
performance, we need to find the right balance between bias and variance.
·
Increasing the complexity of the model can
reduce bias, but it may increase variance.
·
Decreasing the complexity of the model can
reduce variance, but it may increase bias.
·
The bias-variance trade-off can be
visualized as a "U-shaped" curve, where bias decreases and variance
increases as model complexity increases.
·
The optimal point is the point where the
model achieves the best trade-off between bias and variance, and performs best
on new, unseen data.
·
Finding the optimal point may involve
adjusting the model architecture, the training data, or adding regularization
techniques.
Hyperparameters:
·
Hyperparameters are parameters of a
machine learning algorithm that are set by the user, rather than learned from
the data.
·
They are often referred to as
"tuning" parameters because adjusting their values can affect the
performance of the model.
·
Hyperparameters can include things like
learning rates, regularization strengths, number of hidden layers, number of trees,
and kernel types.
·
Choosing the right hyperparameters is
important because they can make the difference between a good model and a great
one.
·
Techniques for selecting hyperparameters
include grid search, random search, and Bayesian optimization.
Examples of
hyperparameters include:
1. Learning
rate:
controls how much the weights of the model are adjusted in each iteration of
the training process.
2. Regularization
strength: controls the degree of regularization applied to
prevent overfitting.
3. Number
of hidden layers: affects the model's ability to capture
complex patterns in the data.
4. Kernel
type: determines the shape of the decision boundary in
support vector machines (SVMs).
5. Batch
size:
the number of training examples used in each iteration of the optimization
algorithm in deep learning..
Regularization is a
technique used in machine learning to prevent overfitting and improve the
generalization performance of the model. Overfitting occurs when the model
learns the noise or random fluctuations in the training data, leading to poor
performance on new, unseen data.
key points about
regularization:
·
Regularization involves adding a penalty
term to the loss function of the model, which discourages the model from
learning overly complex or sensitive relationships between the input and
output.
·
The penalty term can take different forms,
such as L1 or L2 regularization, which encourage sparsity or smoothness in the
weights of the model.
·
Regularization can be applied to a variety
of machine learning algorithms, including linear regression, logistic
regression, support vector machines, and neural networks.
·
The strength of the regularization term is
controlled by a hyperparameter, which is typically set using cross-validation
or other techniques.
·
The purpose of regularization is to
improve the generalization performance of the model, which means it is able to
perform well on new, unseen data.
·
Regularization can help prevent
overfitting by reducing the variance of the model, without increasing the bias.
·
Regularization can also help with feature
selection, by encouraging the model to focus on the most important features and
ignore noisy or irrelevant ones.
·
Regularization is a powerful tool for
improving the robustness and reliability of machine learning models, and is
widely used in practice.
Difference between Overfitting and
Underfitting
|
Overfitting |
Underfitting |
|
Underfitting
occurs when the model is too simple or constrained to capture the patterns in
the training data. |
Overfitting
occurs when the model is too complex or flexible and starts to memorize the
noise or random fluctuations in the training data. |
|
This can happen
when the model is not expressive enough (e.g., too few features, too few
hidden layers), or when the regularization strength is too high. |
This can happen
when the model is too expressive (e.g., too many features, too many hidden
layers), or when the regularization strength is too low. |
|
Underfitting can
lead to high bias and low variance, meaning the model is not able to capture
the important relationships between the input and output. |
Overfitting can
lead to low bias and high variance, meaning the model is too sensitive to the
training data and not able to generalize well to new, unseen data. |
|
The training and
validation errors are both high, indicating that the model is not able to
generalize well to new, unseen data. |
The training
error is low, but the validation error is high, indicating that the model is
overfitting to the training data. |
Limitations of Machine Learning
1. Dependence
on quality and quantity of data: Machine learning models
require large amounts of high-quality data to learn and make accurate
predictions. Limited or low-quality data can lead to inaccurate results.
2. Lack
of transparency: Some machine learning models are
considered "black boxes" because it's challenging to understand how
they arrive at their decisions, making it difficult to troubleshoot issues or
identify biases.
3. Bias
and discrimination: Machine learning models can perpetuate
biases and discrimination if the training data used is biased or if the
algorithm itself has an inherent bias.
4. Overfitting:
Overfitting occurs when a machine learning model becomes too complex and fits
the training data too closely. This can lead to poor performance on new data.
5. Limited
generalizability: Machine learning models may perform well
on the training data but struggle to generalize to new or unseen data.
6. Lack
of human intuition: Machine learning models cannot replicate
human intuition, which can be valuable in decision-making processes.
7. Security
risks: Machine learning models can be vulnerable to attacks,
including adversarial attacks, data poisoning, and model stealing.
8. Cost
and resource-intensive: Machine learning requires
significant computing resources and can be expensive to implement and maintain.
9. Interpretability:
Some machine learning models can be difficult to interpret, making it
challenging to identify the cause of errors or validate results.
10. Ethical
concerns: Machine learning can raise ethical concerns, such as
privacy violations, employment discrimination, and the potential for misuse.
History of Deep Learning
·
Neural Networks in the 1940s:
The idea of neural networks, which are the foundation of deep learning, was
first introduced in the 1940s by Warren McCulloch and Walter Pitts.
·
Backpropagation Algorithm in the
1970s: In the 1970s, the backpropagation algorithm was
developed, which allowed neural networks to learn from data and improve their
accuracy.
·
Convolutional Neural Networks in the
1980s: In the 1980s, Yann LeCun developed convolutional
neural networks (CNNs), which are a type of neural network specifically
designed for image recognition.
·
Recurrent Neural Networks in the
1990s: In the 1990s, recurrent neural networks (RNNs) were
developed, which are able to process sequential data such as language and
speech.
·
Big Data and GPUs in the 2000s:
In the 2000s, the availability of big data and the development of graphical
processing units (GPUs) made it possible to train deeper and more complex
neural networks.
·
ImageNet and Deep Learning Explosion
in 2010s: In 2012, Alex Krizhevsky and his team used deep
learning techniques to win the ImageNet image classification competition, which
sparked a wave of interest and investment in deep learning.
·
Advancements in Deep Learning
Applications in 2010s: In the 2010s, deep learning was
applied to a wide range of fields, including natural language processing,
speech recognition, autonomous driving, and medical diagnosis.
·
Current Developments and Future
Outlook: Current developments in deep learning include
reinforcement learning, generative models, and explainable AI. The future
outlook for deep learning is promising, with continued advancements in hardware
and algorithms expected to drive further progress.
What is Deep Learning :
·
Deep learning is a subfield of machine
learning.
·
It involves the use of artificial neural
networks to model and solve complex problems.
·
The neural network consists of multiple
layers of interconnected nodes that process information in a hierarchical
manner.
·
Deep learning is particularly effective
for tasks such as image recognition, natural language processing, and speech
recognition.
·
It has achieved breakthroughs in a wide
range of fields, including computer vision, robotics, and natural language
processing.
·
Deep learning is widely regarded as one of
the most promising areas of artificial intelligence research.
Advantages of deep learning:
1. State-of-the-art
performance: Deep learning algorithms have achieved
state-of-the-art performance on many tasks, such as image recognition and
natural language processing.
2. Feature
learning: Deep learning models can learn to automatically
extract features from raw data, reducing the need for manual feature
engineering.
3. Scalability:
Deep learning algorithms can be scaled up to handle large datasets and complex
tasks.
4. Flexibility:
Deep learning models can be applied to a wide range of tasks and can be adapted
to new tasks with transfer learning.
Disadvantages of deep learning:
1. Computationally
expensive: Deep learning algorithms require significant
computational resources, such as GPUs, to train effectively.
2. Large
amounts of data: Deep learning models require large
amounts of data to train effectively, which can be a challenge for some
applications.
3. Overfitting:
Deep learning models can be prone to overfitting on the training data, which
can lead to poor performance on new data.
4. Interpretability:
Deep learning models can be difficult to interpret, which can be a challenge
for understanding how decisions are made.
Learning representation of data
·
Learning representation of data refers to
the process of extracting useful features from raw data that can be used by
machine learning algorithms to make accurate predictions. Here are some key
points that explain learning representation of data:
·
Raw data is often too complex and
high-dimensional to be used directly by machine learning algorithms. Therefore,
it needs to be preprocessed to extract useful features.
·
Traditional feature engineering involves
manually selecting and transforming features based on domain knowledge. This
approach can be time-consuming and may not always lead to the best features.
·
Deep learning algorithms, on the other
hand, can automatically learn representations of data through a process called
feature learning. This involves training a neural network to extract features
from the raw input data.
·
Feature learning can be unsupervised or supervised.
In unsupervised learning, the neural network is trained to learn patterns and
structure in the data without explicit labels. In supervised learning, the
neural network is trained to learn features that are relevant to a specific
prediction task.
·
Deep learning models can learn
hierarchical representations of data, where features at higher layers of the
model capture more abstract and complex concepts. This can lead to better
performance on tasks such as image recognition, speech recognition, and natural
language processing.
·
Learning representations of data can also
be used for transfer learning, where a pre-trained model is used as a starting
point for a new prediction task. This can significantly reduce the amount of
training data required and improve the accuracy of the model.
·
In summary, learning representation of
data involves automatically extracting useful features from raw data using deep
learning algorithms, leading to better performance on prediction tasks and
enabling transfer learning.
Understanding How Deep Learning works in Three
Figures:
First figure:
·
A neural network is composed of layers of
interconnected "neurons"
·
Each neuron in a layer receives input from
the previous layer, processes it, and sends it on to the next layer
·
The input layer receives the raw data, and
the output layer produces the final output of the network
·
The layers in between the input and output
layers are called "hidden layers," and they are used to extract
features and representations of the data
·
Deep neural networks have several hidden
layers, allowing them to learn more complex features and relationships between
inputs and outputs
Second figure:
·
In supervised learning, a dataset with
labeled examples is used to train the network
·
The network is presented with inputs and
the corresponding desired outputs
·
Its weights and biases are adjusted to
minimize the difference between the network's predictions and the desired
outputs
·
This process is repeated for many examples
in the dataset
·
The network gradually learns to make
accurate predictions on new, unseen examples
Third figure:
·
Forward and backward propagation is the
process of passing input data through the layers of the neural network and
computing the output
·
Adjusting the weights of the network in
the backward pass is done by using an optimization algorithm, like Stochastic
Gradient Descent (SGD)
·
The network learns from data by minimizing
the error between the predicted output and the actual output through the
optimization process
Common Architectural Principles of Deep
Learning:
Principles:
·
Use of multiple layers:
Deep networks have multiple layers of neurons, allowing them to learn more
complex and abstract representations of the input data.
·
Non-linear activation function:
Each neuron in a deep network applies a non-linear activation function to the
output of the previous layer. This allows the network to learn non-linear
relationships between inputs and outputs.
·
Gradient-based learning:
Deep networks are trained using a gradient-based optimization algorithm, such
as stochastic gradient descent. This involves computing the gradient of the
loss function with respect to the network parameters and updating them in the
direction of the negative gradient.
·
Backpropagation:
The gradients are computed using backpropagation, which is an efficient
algorithm for computing the gradients of the loss function with respect to each
parameter in the network.
·
Dropout:
Dropout is a regularization technique used in deep networks to prevent
overfitting. It involves randomly dropping out some of the neurons during
training, forcing the remaining neurons to learn more robust representations of
the input data.
·
Batch normalization:
Batch normalization is another regularization technique used in deep networks
to improve training stability and performance. It involves normalizing the
inputs to each layer to have zero mean and unit variance.
·
Convolutional layers:
Convolutional layers are specialized layers used in deep networks for
processing images and other spatial data. They apply a set of learned filters
to the input, producing a set of feature maps.
·
Recurrent layers:
Recurrent layers are specialized layers used in deep networks for processing
sequential data, such as text or speech. They maintain an internal state that
allows them to capture temporal dependencies in the input data.
By using these architectural principles, deep neural
networks can learn complex relationships in data and achieve state-of-the-art
performance in a wide range of applications, from image and speech recognition
to natural language processing and autonomous driving
Architecture Design:
The architecture design of a deep neural network
involves several decisions such as:
·
Number of layers: Deep neural networks
have multiple hidden layers. Deciding on the number of layers requires
balancing the need for more complex representations with the risk of
overfitting.
·
Number of neurons: The number of neurons
in each layer determines the network's capacity to represent more complex features.
This decision also requires balancing the need for more neurons with the risk
of overfitting.
·
Type of activation function: Activation
functions introduce non-linearity into the neural network, enabling it to learn
complex functions. Common activation functions include sigmoid, ReLU, and tanh.
·
Type of layer: Different types of layers
serve different purposes. For example, convolutional layers are used in
computer vision tasks to extract features from images, while recurrent layers
are used in natural language processing tasks to model sequences of data.
·
Strategy for regularization:
Regularization techniques such as dropout and L2 regularization are used to
prevent overfitting and improve generalization performance.
·
Optimization algorithm: Gradient-based
optimization algorithms such as stochastic gradient descent (SGD) are used to
optimize the network's parameters during training.
The design of a deep neural network architecture
involves making informed decisions based on the characteristics of the problem
being solved, the size and complexity of the data, and the available
computational resources.
Applications of Deep Learning:
1. Computer
Vision: Deep learning is widely used for image and video
recognition, object detection, face recognition, and self-driving cars.
2. Natural
Language Processing (NLP): Deep learning is used for language
translation, sentiment analysis, speech recognition, and chatbots.
3. Healthcare:
Deep learning is used for medical image analysis, drug discovery, and disease
diagnosis.
4. Finance:
Deep
learning is used for fraud detection, risk management, and stock price
prediction.
5. Gaming:
Deep learning is used for game playing, character animation, and game AI.
6. Robotics:
Deep learning is used for object recognition, navigation, and control of
robots.
7. Marketing:
Deep learning is used for customer segmentation, personalized marketing, and
recommendation systems.
8. Agriculture:
Deep learning is used for crop yield prediction, disease detection, and
precision farming.
These are just a few examples, and the applications of deep learning are constantly growing and evolving.
Introduction and Use of Popular Industry
Tools for Machine Learning and Deep Learning :
Popular industry tools such as TensorFlow, Keras,
PyTorch, Caffe, and Shogun are used for building and training deep learning
models. These tools provide a user-friendly interface and a high-level
programming language to implement complex deep learning architectures with
ease. Here is a brief introduction to each tool:
1. TensorFlow:
TensorFlow is an open-source software library developed by Google Brain Team
for numerical computation and large-scale machine learning. It offers a variety
of tools and libraries for building and training deep learning models.
2. Keras:
Keras is a high-level neural network API that can run on top of TensorFlow,
Theano, or CNTK. It provides a simple and easy-to-use interface for building
and training deep learning models.
3. PyTorch:
PyTorch is an open-source machine learning library developed by Facebook's AI
research team. It provides a dynamic computational graph that allows for easy
experimentation and debugging.
4. Caffe:
Caffe is a deep learning framework developed by Berkeley AI Research (BAIR). It
is primarily used for image classification tasks and is known for its fast
training speed.
5. Shogun:
Shogun is an open-source machine learning library that supports a variety of
algorithms and data types. It provides a modular and flexible architecture for
building and training deep learning models.
Each tool has its own strengths and weaknesses, and
the choice of tool often depends on the specific application and the user's
preferences and expertise.
