Deep Learning: In a Nutshell

Summary

This script delves into the relationship between deep learning and machine learning, highlighting that deep learning is a subset of the latter. It explains the fundamental concepts of machine learning, such as algorithms, loss functions, and the importance of gradient descent for optimization. The script further clarifies the role of activation functions in introducing non-linearity to neural networks, enabling them to handle complex data. The video concludes by emphasizing the distinct capabilities of machine learning and deep learning, with the latter's versatility in tasks like image classification and natural language processing.

Takeaways

• 🧠 Deep learning is a subset of machine learning, derived from its principles.
• 🔢 Machine learning algorithms use a loss function to measure prediction error and optimize it for higher accuracy.
• 📉 The mean squared error is a common loss function used in linear regression to calculate the difference between predictions and actual values.
• 🔍 Gradient descent is a key optimization technique in machine learning, used to minimize the loss function by iteratively adjusting parameters.
• 🔧 Deep learning often employs advanced variants of gradient descent, such as Adam, for optimization.
• 🔄 The initialization of parameters in machine learning models is crucial as it can affect the convergence to global or local minima.
• 🌟 Keras provides various initializers to help find efficient starting points for model parameters.
• 💡 Activation functions introduce non-linearity into neural networks, allowing them to model complex patterns in data.
• 📊 Sigmoid is an example of an activation function that maps inputs to a range between 0 and 1, useful for logistic regression.
• 🔗 Deep learning builds upon fundamental machine learning concepts, such as neurons and activation functions, to create complex models.
• 🚀 Deep learning's flexibility with activation functions, initializers, regularizers, and optimizers allows it to tackle more complex tasks than traditional machine learning.

Q & A

What is the relationship between deep learning and machine learning?

Deep learning is a subset of machine learning, meaning it is derived from the principles of machine learning and focuses on neural networks with many layers.

What is the fundamental role of an algorithm in machine learning?

The fundamental role of an algorithm in machine learning is to take input 'X', make predictions, and, in the case of supervised learning, compare these predictions with actual output 'y' to measure accuracy and optimize itself to reduce errors.

How does a loss function contribute to the optimization of a machine learning model?

A loss function measures the error made by the algorithm. It informs the model how far off its predictions are from the actual values, and the optimizer uses this information to adjust the model parameters and minimize the loss.

What is the mean squared error, and how is it used in linear regression?

The mean squared error is a loss function used in linear regression that calculates the average of the squares of the differences between the predicted and actual values. It is used to quantify the error and guide the optimization process.

How does gradient descent work in optimizing the loss function?

Gradient descent is an optimization algorithm that iteratively moves in the direction of the steepest descent as defined by the negative of the gradient to minimize the loss function.

What is the significance of the starting point in gradient descent?

The starting point in gradient descent is significant because it can lead to different local or global minima. A poor starting point may cause the algorithm to get stuck in a local minimum, while a good starting point can help find the global minimum more efficiently.

What are some advanced optimizers used in deep learning apart from gradient descent?

Advanced optimizers used in deep learning include successors of gradient descent, such as Adam, which can provide more efficient ways to minimize the loss function compared to traditional gradient descent.

How does a neural network's structure relate to the fundamental concepts of machine learning?

A neural network's structure is built upon the fundamental concepts of machine learning, such as neurons performing calculations similar to linear regression, with the addition of non-linearity introduced by activation functions.

What is the purpose of an activation function in a neural network?

The purpose of an activation function in a neural network is to introduce non-linearity to the model, allowing it to learn and fit more complex patterns in the data.

What is the difference between epochs and max_iter in the context of machine learning and deep learning?

Epochs and max_iter both refer to the number of iterations or passes through the entire dataset during training. In machine learning, it is often referred to as max_iter, while in deep learning, it is called epochs.

Why is machine learning not considered equal to deep learning, despite their similarities?

Machine learning is not equal to deep learning because while machine learning can handle tabular data, deep learning is designed to handle more complex tasks such as image classification, natural language processing, and other tasks that require understanding intricate patterns in large datasets.

Outlines

00:00

🤖 Fundamentals of Machine Learning and Deep Learning

This paragraph introduces the relationship between deep learning and machine learning, emphasizing that deep learning is a subset of machine learning. It explains the commonality among machine learning algorithms, which includes taking input 'X' and producing a prediction, with supervised learning algorithms also comparing predictions to actual outputs 'y' to measure accuracy. The concept of the loss function is introduced as a way to quantify error, using mean squared error as an example. The role of the optimizer, particularly gradient descent, in minimizing the loss function is discussed. The paragraph also touches on the importance of initialization in the performance of algorithms and mentions various optimizers used in deep learning, such as Adam. The summary concludes by highlighting the fundamental role of loss functions and gradient descent in optimizing machine learning models.

05:02

🚀 Applications and Distinctions of Deep Learning

The second paragraph delves into the applications of deep learning, illustrating its ability to handle complex datasets for tasks like predicting the next word in a sentence, autonomous driving, and even landing a rover on another planet. It clarifies that machine learning and deep learning are not synonymous, with machine learning being more suited for tabular data and deep learning capable of handling more complex tasks such as image classification and natural language processing. The paragraph underscores the advantages of deep learning, such as the ability to choose various activation functions, initializers, regularizers, and optimizers, as well as the flexibility in designing the architecture of neural networks. The summary ends by inviting viewers to explore more about the discussed algorithms in the 'Under the hood of machine learning' series and to check out related project videos for a deeper understanding of the concepts.

Keywords

💡Deep Learning

Deep Learning is a subset of machine learning that involves the use of artificial neural networks with multiple layers to model and understand complex patterns in data. It is central to the video's theme as it is contrasted with machine learning to illustrate the relationship and differences between the two. The script mentions deep learning's ability to handle complex tasks such as natural language processing and image classification.

💡Machine Learning

Machine Learning is a broader field of study that gives computers the ability to learn from and make predictions or decisions based on data. The script positions machine learning as an umbrella term that includes deep learning, emphasizing its foundational role in the development of algorithms that can learn from input data, such as in supervised learning scenarios.

💡Algorithm

An algorithm in the context of the video refers to a set of rules or procedures used by machine learning models to process input data and produce predictions. The script explains that various algorithms in machine learning share a common goal of minimizing error through optimization, which is a key concept in understanding how these models improve over time.

💡Supervised Learning

Supervised Learning is a type of machine learning where an algorithm is trained on labeled data, meaning the input data comes with the correct output. The script uses this concept to explain how algorithms compare their predictions to actual values to measure accuracy and optimize their performance accordingly.

💡Loss Function

A loss function is a measure of how well the model's predictions match the actual data. The script describes the loss function as a critical component in training machine learning models, as it quantifies the error made by the model, guiding the optimization process through feedback on prediction accuracy.

💡Gradient Descent

Gradient Descent is an optimization algorithm used to minimize the loss function by iteratively adjusting the model's parameters. The script explains that gradient descent is fundamental to machine learning and has variants in deep learning, such as Adam, which aim to find the minimum value of the loss function more efficiently.

💡Optimizer

An optimizer in machine learning is responsible for adjusting the parameters of the model to minimize the loss function. The script mentions that while gradient descent is a common optimizer in machine learning, deep learning offers a variety of optimizers that can improve the training process.

💡Activation Function

An activation function is a mathematical function applied to the output of a neuron to introduce non-linearity into the model, allowing it to learn more complex patterns. The script uses the sigmoid function as an example, illustrating how it can transform a linear model into one capable of fitting non-linear data.

💡Neural Network

A neural network is a series of algorithms modeled loosely after the human brain that are designed to recognize patterns. The script describes the structure of a neural network, emphasizing how each neuron performs calculations using weights and biases, and how these networks can be used to approximate complex datasets.

💡Epoch

In the context of the video, an epoch refers to a complete iteration over the training dataset. The script explains that epochs are a measure of how many times the learning algorithm will work through the entire training dataset, which is crucial for understanding the training process in both machine and deep learning.

💡Initializer

An initializer in deep learning is a method used to set the initial values of the model's parameters. The script discusses the importance of initializers in helping the model to converge faster by avoiding local minima, which is a significant aspect of optimizing neural network training.

Highlights

Deep learning is a subset of machine learning, derived from its principles.

Machine learning algorithms share a common fundamental aspect of taking input X and predicting output.

Supervised learning algorithms use actual output 'y' to measure model accuracy.

The loss function is crucial for optimizing algorithms by indicating prediction errors.

Linear regression uses mean squared error as its loss function.

Gradient descent is the primary optimizer used in machine learning to minimize the loss function.

Different optimizers like Adam are available in deep learning as successors to gradient descent.

Gradient descent aims to find the minimum value of a function, which represents the least error.

The starting point in gradient descent can significantly affect the outcome, potentially leading to local vs. global minima.

Proper initialization techniques in Keras help find efficient starting positions for faster convergence.

Loss functions, gradient descent, and initialization are fundamental to both machine learning and deep learning.

Neural networks consist of neurons performing calculations based on weights, biases, and activation functions.

Activation functions introduce non-linearity, allowing neural networks to fit complex data.

Sigmoid activation function maps inputs to a range between 0 and 1, useful for logistic regression.

Deep learning builds upon the fundamental concepts of machine learning, applying them to more complex structures.

The iterative process of error calculation and optimization continues for multiple epochs in both fields.

Deep learning's flexibility with activation functions, initializers, regularizers, and optimizers allows it to handle more complex tasks.

Machine learning is suited for tabular data, while deep learning excels in image classification, translation, and natural language processing.

The video provides a comprehensive understanding of the relationship and differences between machine learning and deep learning.

Transcripts

Deep learning and machine learning seem very diverse fields but we have seen in this popular

diagram that deep learning is a subset of machine learning which means deep learning

is derived from machine learning.

But to actually understand what is Deep learning we have to understand this connecting link

between them.

There are many Algorithms in machine learning but each of them has one fundamental thing

in common let's see that.

This box represents an algorithm that takes X as the input and outputs the prediction

but for the supervised learning algorithm, we also give actual output 'y' as the reference

so it can compare its prediction with actual values and can see how right it is which is

known as the accuracy of the model!

If the results are bad it tries to optimize itself to make this percentage higher so how

this algorithm is able to do that?

Because of the Loss function.

The loss function tells how much is the error made by our algorithm in the case of Linear

regression this loss function is the mean squared error.

For example, If the prediction is 10 and the actual answer should be 8 then the mean squared

error will be the actual output minus prediction squared which will be 4.

So our actual aim is to reduce this loss as much as possible.

This will be done by the optimizer in machine learning we only use Gradient descent to optimize

the Loss function but in deep learning, we can use various successors of the Gradient

descent like Adam.

In my 'under the hood of ML' series, we have implemented Ridge, Lasso, and Elastic Net

using Gradient descent.

Generally, Gradient descent aims to find the minimum value of a function.

In this graph, the minimum value is 0.

So, we will make our first guess at this point therefore gradient descent will start finding

the minimum from this point.

And eventually, after some iteration, it will find the minimum point.

Like this, Gradient descent can minimize any function.

In our case, that function is "loss function".

But when we have a function like this where there is more than one valley in our case

there are 2 valleys the minimum point here is a local minimum and the point here is the

Global minimum.

If we take our starting guess here and run the gradient descent it will be able to find

the Global minimum but if we just shift the starting point a little and run gradient descent

again it will just get stuck in the local minimum.

So, just initializing the point here instead of here made a big difference in the performance

of our algorithm.

To show you it, in reality, I have created a Gradient descent algorithm from scratch

and I have passed it the same function as I showed earlier now this x_init here means

the starting position of x from where it will start the gradient descent.

If I set it to 4.7 and run the program it was able to find the Global minimum but if

I set it to 4.4 and run the program again it just ends up being stuck in the local minimum

this is one of the problems faced by many ML and DL algorithms but to overcome this

there are many types of initializer in Keras which help to find the starting position of

X efficiently so our algorithm converges faster!

Now to summarize, we have seen how loss functions tell the error made by our algorithm and how

gradient descent optimizes loss function to reduce the error.

Also, how much the initialization matters.

If you are well familiar with Deep learning you might find all this very familiar because

as I said deep learning is derived from machine learning.

So, now let us find that connecting link!

This is a normal neural network structure you may have seen in many diagrams... each

of these circle represent a neuron which is doing some kind of calculation if we only

see one out of them and see what it is doing we can see it is multiplying are x with the

weights plus the bias which is also written in the TensorFlow documentation of Dense layer.

If you can connect this formula is of Linear regression.

But when we work with more complicated data we want non-linearity this is when Activation

functions pop in.

This thing here is nothing but an activation function which do some operation on our output

to make it fit in non-linear data.

Now, there are many activation functions but one of them is the sigmoid activation.

If I write the formula of the sigmoid function and draw the graph you can see it takes x

as input and maps it in the range 0 to 1.

Now after applying this to our neuron it can fit on non-linear data.

This is nothing but the Logistic regression.

The thing I am trying to convey here is deep learning is made out of the very fundamental

things of machine learning.

Now, to join into our previous diagram we can say that this y_pred value goes to loss

function where it finds the error and according to that error gradient descent searches for

better values and give it to the algorithm and this loop will continue for 'n epochs'

or 'n iteration' in machine learning we call it as max_iter and in deep learning we call

it as epochs nothing much changes.

Now, just imagine all these neurons are working in the same way.

That's why when they all work in coordination they can approximate very complex datasets.

Like predicting the next word in a sentence, driving a car also known as an Autonomous

vehicle till landing a rover on a whole different planet.

So, is machine learning equal to deep learning?

The answer is no.

They are similar but not equal because machine learning is good at dealing with tabular data

and deep learning can deal with more intense stuff like classifying animals in images,

translation, natural language processing, like that.

Because deep learning lends you the power of choosing activation functions, initializers,

regularizers, optimizers, and how many layers and neurons you want in every layer.

This was it for today's video!

You can check my Under the hood of machine learning series to know more about the algorithms

we discussed today.

Also, I have many projects videos like the Next word predictor, Wake word detection,

and Image keywording.

So, check it out.

Thanks for watching.