1- Deep Learning (for Audio) with Python: Course Overview

Summary

Welcome to 'Deep Learning for Audio,' a course designed to demystify deep learning's capabilities and limitations. We'll delve into neural network theory, covering the math behind these algorithms, and explore coding with TensorFlow. The course focuses on audio and music, introducing various neural network types like CNNs and RNNs. Expect a mix of theory, coding tutorials, and real-world applications. Aimed at developers, especially those interested in audio, this course assumes some coding and math proficiency but is open to all looking to enhance their AI skills.

Takeaways

• 🧠 **Understanding Deep Learning**: The course aims to help you understand the capabilities and limits of deep learning.
• 📚 **Fundamental Theory**: You will learn the fundamental theory behind neural networks, including some essential math.
• 💻 **Practical Coding**: The course includes practical sessions where you'll code deep learning networks using industry-standard libraries like TensorFlow.
• 🎓 **Exploring Neural Networks**: You'll explore different types of neural networks such as RNNs, CNNs, and GANs.
• 🎵 **Audio and Music Focus**: Although the course focuses on audio and music, it's suitable for anyone interested in deep learning, regardless of their interest in audio.
• 🐍 **Python and TensorFlow**: The course uses Python and TensorFlow, which are industry standards for AI and machine learning.
• 🔧 **Hands-on Experience**: You'll gain hands-on experience with coding tutorials and real-world applications.
• 📈 **Neural Network Types**: The course covers various neural network types starting from MLPs to CNNs, RNNs, and GANs.
• 📊 **Mathematical Foundation**: Basic linear algebra and derivatives will be taught to understand how neural networks operate.
• 🔗 **Resources and Materials**: All course materials, including code and slides, will be available on a GitHub page.
• 👨‍💻 **Target Audience**: The course is designed for developers, data analysts, and practitioners with some experience who want to learn deep learning, especially related to audio and music.

Q & A

What are the learning goals of the 'Deep Learning for Audio' course?

The learning goals include understanding the capabilities and limits of deep learning, learning the fundamental theory behind neural networks, coding deep learning networks using libraries like TensorFlow, and exploring different types of neural networks with a focus on audio and music.

Why is TensorFlow chosen as the primary deep learning library for this course?

TensorFlow is chosen because it is an industry standard for artificial intelligence, used widely in startups, corporations, and academia. It offers a high-level interface called Keras that allows for creating complex networks with minimal code, and it is open source.

What are the different types of neural networks that will be covered in the course?

The course will cover Multi-Layer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs).

What is the expected background for the course participants?

The course is designed for developers who want to learn deep learning skills, especially those with an interest in audio and music. It is not for Python beginners but for those with intermediate coding skills and some understanding of basic linear algebra and digital signal processing.

How will the course content be structured?

The content will be structured into three blocks: theory, coding tutorials, and real-world applications. The theory section will cover basics like linear algebra and derivatives, coding tutorials will involve both Python and TensorFlow, and real-world applications will test the knowledge acquired.

What kind of math will be covered in the course?

The course will cover basic linear algebra and derivatives, which are essential for understanding how neural networks work.

Who is the course suitable for besides developers interested in audio and music?

The course is also suitable for data analysts who want to learn more about machine learning and AI, and practitioners with experience in audio or digital signal processing who want to advance their skills.

How can participants access the code and slides from the course?

The instructor will post all the lessons, including code and slides, on a GitHub page, which will be linked in the description of each video in the series.

What is the course's stance on the necessity of prior knowledge in audio digital signal processing?

While having knowledge of audio digital signal processing is beneficial, it is not necessary as the course will cover all the necessary DSP concepts.

Is the course designed for absolute beginners in programming?

No, the course is not designed for absolute beginners in programming. It assumes participants have intermediate coding skills, specifically in Python.

What is the main focus of the course in terms of content?

The main focus is on deep learning, with an emphasis on audio and music applications. The course will not teach basic coding but will focus on AI concepts and their implementation using deep learning techniques.

Outlines

00:00

🎓 Introduction to Deep Learning for Audio

This paragraph introduces the course 'Deep Learning for Audio,' emphasizing the learning goals which include understanding the capabilities and limits of deep learning, grasping the fundamental theory behind neural networks, and learning the math that powers these algorithms. The course will also cover practical aspects like coding deep learning networks using industry-standard libraries such as TensorFlow. The focus on audio and music is highlighted, and it's mentioned that while the course is tailored for those interested in audio, the deep learning concepts taught are applicable universally. The technologies to be used, namely Python and TensorFlow, are introduced, with reasons for their selection being their industry standard status and TensorFlow's high-level interface, Keras, which simplifies creating complex networks.

05:02

👨‍🏫 Course Content and Target Audience

The second paragraph delves into the course content, outlining that it will cover an introduction to artificial intelligence, machine learning, and deep learning, followed by an exploration of different types of neural networks including multi-layer perceptrons, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). The course structure is divided into three parts: theory, coding tutorials, and real-world applications. The paragraph also addresses where to find the course code and slides, which will be available on a GitHub page. The target audience for the course is specified as developers looking to acquire deep learning skills, especially those with an interest in audio and music, as well as data analysts wanting to expand their knowledge into AI and machine learning. The course is not intended for Python beginners but rather for those with intermediate coding skills and some understanding of linear algebra, though the latter is not mandatory as the necessary math will be covered.

Keywords

💡Deep Learning

Deep Learning is a subset of machine learning that focuses on artificial neural networks with representation learning. In the context of the video, deep learning is the central theme, with the course aiming to teach the capabilities and limits of deep learning in audio processing. The script mentions that the course will cover the fundamental theory behind neural networks, which are the building blocks of deep learning.

💡Neural Networks

Neural networks are a series of algorithms modeled loosely after the human brain that are designed to recognize patterns. They are a core component of deep learning. The video script discusses learning the theory behind these networks, which are essential for understanding how deep learning algorithms work, particularly in the domain of audio and music.

💡TensorFlow

TensorFlow is an open-source software library for data flow and differentiable programming across a range of tasks, but it is predominantly used for machine learning and deep learning applications. The script highlights that TensorFlow will be used as the industry standard deep learning library for coding deep learning networks, emphasizing its widespread use in startups, corporations, and academia.

💡Convolutional Neural Networks (CNNs)

CNNs are a class of deep, feed-forward artificial neural networks most commonly applied to analyzing visual imagery. In the script, CNNs are mentioned as one of the types of neural networks that will be explored, particularly for their utility in processing images and audio, which is relevant to the course's focus on audio.

💡Recurrent Neural Networks (RNNs)

RNNs are a class of neural networks that are designed to recognize patterns in sequences of data, such as time series data or natural language. The video script includes RNNs in the list of neural networks to be covered, highlighting their importance in handling sequential data, which is pertinent to audio and music processing.

💡Generative Adversarial Networks (GANs)

GANs are a class of models in machine learning consisting of two neural networks, designed to generate new data with the same statistics as existing data. The script notes that GANs are a trendy topic and will be explored in the course, likely in the context of their innovative applications in audio and music generation.

💡Python

Python is a high-level, interpreted, and general-purpose programming language that is widely used for its simplicity and readability. The script mentions Python as the programming language of choice for the course, underlining its status as an industry standard for AI and machine learning, and its compatibility with TensorFlow.

💡Audio Data

Audio data refers to the digital representation of sound waves, which can be processed and analyzed by computers. The video's focus on audio is emphasized through the mention of audio data, indicating that the course will delve into how deep learning can be applied to analyze and generate audio signals.

💡Digital Signal Processing (DSP)

DSP is the study and use of digital processes for the analysis, manipulation, and synthesis of signals. While not explicitly defined in the script, DSP is mentioned as a relevant field for those interested in audio and music, suggesting that the course will touch upon DSP concepts in the context of deep learning for audio.

💡Industry Standards

Industry standards refer to the norms or requirements to which the whole industry agrees. In the script, the presenter chooses Python and TensorFlow because they are industry standards for AI, implying that learning these will prepare students for real-world applications and job requirements in the field.

💡GitHub

GitHub is a web-based hosting service for version control and source code management, using Git. It is mentioned in the script as the platform where the course materials, including code and slides, will be made available for students, facilitating easy access and collaboration.

Highlights

Introduction to deep learning for audio

Understanding the capabilities and limits of deep learning

Learning the fundamental theory behind neural networks

Mathematical foundations of deep learning algorithms

Practical coding with deep learning libraries like TensorFlow

Exploration of different types of neural networks

Focus on audio and music applications

Technologies used: Python and TensorFlow

Advantages of TensorFlow for industry and academia

High-level interface of TensorFlow called Keras

Content includes an intro to AI, machine learning, and deep learning

Different flavors of neural networks: MLP, CNN, RNN, GAN

Learning style includes theory, coding tutorials, and real-world applications

Course material available on GitHub

Target audience: developers, practitioners, and data analysts

Prerequisites: intermediate coding skills, not for Python beginners

Course covers necessary math and DSP for neural networks

Course not necessary for those with extensive DSP knowledge

Transcripts

[Music]

hi everybody and welcome to deep

learning for audio with - in this course

we're gonna learn a lot about deep

learning so what about the learning

goals so first of all I want you to

understand the capabilities and limits

of deep learning so what's possible and

what's not possible then after that

we're gonna learn a lot about the

fundamental theory behind neural

networks we're gonna learn a little bit

about the math for example that powers

this very powerful algorithms and then

we're gonna move on to more practice

based stuff and we're gonna learn how to

code deep learning networks using

industry standards deep learning

libraries like tensor flows and then

obviously we're gonna play around with a

bunch of different types of knit neural

networks so like IRA names CN NS that

we're gonna learn what all these

acronyms like really stand for cool the

one thing that you should understand

about this course is that its focus is

on audio and music now can you follow

this course if even if you're not

interested in audio at all yes you can

because at the end of the day this is a

deep learning course and so you're gonna

learn all the theory and implementation

about deep learning but bear in mind

that all the examples I'll get or most

of the examples I should say are gonna

be using audio data or music now what

about the technologies that we're gonna

use so obviously we're gonna use Python

and on top of that we're gonna use

tensorflow

so why did I choose both technologies

right so Python and tensorflow are both

industry standards for artificial

intelligence so if you're trying to like

pick up a job in AI or machine learning

obviously you already know that - like

is the way to go you're gonna be

required to know or learn Python and

then obviously on top of that there's

this super nice deep learning library

called tensorflow which is

used almost everywhere in startups at

corporations and even in academia and

for doing research now the great thing

about tensorflow

is that on top of tensorflow you have

kind of like high-level interface that's

called carers that enables you to create

very complex networks using very little

code so that's fantastic and that's very

nice like just like to get started with

deep learning and finally another reason

why we are gonna use tensorflow is

become is because it is open source and

so if you want to check things around

you actually can now what about the

content so what are we gonna actually

learn so you're gonna get an intro to

artificial intelligence machine learning

and deep learning so you were gonna kind

of like learn the differences and the

overlaps of these different fields and

subfields but then after that we're

gonna move on and jump into the

different flavors of neural networks

that are out there so we'll start with

something that's been like historically

the initial network that has been widely

adopted and that's the multi-layer

perception then after that we're gonna

get into convolutional neural networks

or cnn's

and you may be familiar at least like

with this acronym and these networks are

super useful for doing processing with

images or and also like with audio and

then we're gonna jump onto recurrent

neural networks or are announced and

these are fantastic algorithms that you

won't use for predicting like time

series and for handling time series

types of data and then finally we're

going to look into guns or generative

adversarial networks that are super

fashionable these days so what should

you expect from this course what type of

style in terms of like the learning well

we're gonna have three different blocks

I would say well we're gonna learn quite

a lot about the theory now I'm not gonna

go super deep into math because at the

end of the day this is not a math course

but you're gonna learn

quite a lot about basic linear algebra

and derivatives and these kind of things

because we need them to understand how

neural networks work and how to treat

them in order to have like very

effective like algorithms like for

solving our problems now we're going to

use all of this theory and we are going

to implement that and so basically we're

gonna have a bunch of different coding

tutorials where we're gonna use both -

for coding neural networks from scratch

but then on top of that we're gonna have

tensor flow code where we're going to

create very complex neural networks now

the third part of this is we're gonna

have a bunch of different applications

kind of real word applications I would

say where we're gonna test all of the

knowledge that we've acquired from

theory and basic tensorflow code so

obviously this is like a very important

question so where'd you get code and

slides so I'm gonna have a github page

like my github page and I'm gonna post

all of this lessons online and so you

can just like browse them and download

what you need and obviously all of this

information is going to be below in the

description of each video in the series

cool so who's this cause for now when I

designed this course I had in mind

- developers who want to pick up deep

learning skills and so this is not a

course for beginners rather like for

actual developers and also this if you

are like a dev who's already playing

around with a bunch of this deep

learning libraries like tensorflow for

example but you want to learn more about

how you can show how like neural

networks really work like under the hood

so this is really perfect for you

because you're gonna get like a an

understanding like at a deeper level now

obviously this course is also very

useful for devs who have an interest in

audio and music because at the end of

the day you're gonna be introduced to AI

music and AI

Audio and if you are a practitioner

who's got some experience in audio

digital signal processing or DSP and you

want to step up your game even more and

get into a Y again this is like the

right course for you and finally I think

like another category who would benefit

quite a lot from this course are a data

analysts who want to learn more about

machine learning and who want to learn

more about AI as well and how to get

things out as I mentioned this is not a

course for Python beginners rather you

should have some intermediate coding

skills because at the end of the day and

not gonna teach you how to code the

focus of this course is on AI not coding

itself now if you know quite a lot about

basic linear algebra that's fantastic

but it's definitely not necessary

because I'm gonna cover all the math

we'll need to understand neural networks

at the same time if you know about audio

digital signal processing that's

fantastic but it's not really necessary

because again I'm gonna cover all the

DSP stuff that we really need cool so

this was it for the course overview so

now just brace yourself deep learning is

coming by