A deep understanding of deep learning (with Python intro) from Udemy

Deep learning is increasingly dominating technology and has major implications for society.

From self-driving cars to medical diagnoses, from face recognition to deep fakes, and from language translation to music generation, deep learning is spreading like wildfire throughout all areas of modern technology.

But deep learning is not only about super-fancy, cutting-edge, highly sophisticated applications. Deep learning is increasingly becoming a standard tool in machine-learning, data science, and statistics. Deep learning is used by small startups for data mining and dimension reduction, by governments for detecting tax evasion, and by scientists for detecting patterns in their research data.

Deep learning is now used in most areas of technology, business, and entertainment. And it's becoming more important every year.

How does deep learning work?

Deep learning is built on a really simple principle: Take a super-simple algorithm (weighted sum and nonlinearity), and repeat it many many times until the result is an incredibly complex and sophisticated learned representation of the data.

Is it really that simple? mmm OK, it's actually a tiny bit more complicated than that ;) but that's the core idea, and everything else literally everything else in deep learning is just clever ways of putting together these fundamental building blocks. That doesn't mean the deep neural networks are trivial to understand: there are important architectural differences between feedforward networks, convolutional networks, and recurrent networks.

Given the diversity of deep learning model designs, parameters, and applications, you can only learn deep learning I mean, really learn deep learning, not just have superficial knowledge from a youtube video by having an experienced teacher guide you through the math, implementations, and reasoning. And of course, you need to have lots of hands-on examples and practice problems to work through. Deep learning is basically just applied math, and, as everyone knows, math is not a spectator sport.

What is this course all about?

Simply put: The purpose of this course is to provide a deep-dive into deep learning. You will gain flexible, fundamental, and lasting expertise on deep learning. You will have a deep understanding of the fundamental concepts in deep learning, so that you will be able to learn new topics and trends that emerge in the future.

Please note: This is not a course for someone who wants a quick overview of deep learning with a few solved examples. Instead, this course is designed for people who really want to understand how and why deep learning works; when and how to select metaparameters like optimizers, normalizations, and learning rates; how to evaluate the performance of deep neural network models; and how to modify and adapt existing models to solve new problems.

You can learn everything about deep learning in this course.

In this course, you will learn

Theory: Why are deep learning models built the way they are?
Math: What are the formulas and mechanisms of deep learning?
Implementation: How are deep learning models actually constructed in Python (using the PyTorch library)?
Intuition: Why is this or that metaparameter the right choice? How to interpret the effects of regularization? etc.
Python: If you're completely new to Python, go through the 8+ hour coding tutorial appendix. If you're already a knowledgeable coder, then you'll still learn some new tricks and code optimizations.
Google-colab: Colab is an amazing online tool for running Python code, simulations, and heavy computations using Google's cloud services. No need to install anything on your computer.

Unique aspects of this course

Clear and comprehensible explanations of concepts in deep learning, including transfer learning, generative modeling, convolutional neural networks, feedforward networks, generative adversarial networks (GAN), and more.
Several distinct explanations of the same ideas, which is a proven technique for learning.
Visualizations using graphs, numbers, and spaces that provide intuition of artificial neural networks.
LOTS of exercises, projects, code-challenges, suggestions for exploring the code. You learn best by doing it yourself.
Active Q&A forum where you can ask questions, get feedback, and contribute to the community.
8+ hour Python tutorial. That means you don't need to master Python before enrolling in this course.

So what are you waiting for??

Watch the course introductory video and free sample videos to learn more about the contents of this course and about my teaching style. If you are unsure if this course is right for you and want to learn more, feel free to contact with me questions before you sign up.

I hope to see you soon in the course.

Mike

What's inside

Learning objectives

The theory and math underlying deep learning
How to build artificial neural networks
Architectures of feedforward and convolutional networks
Building models in pytorch
The calculus and code of gradient descent
Fine-tuning deep network models
Learn python from scratch (no prior coding experience necessary)
How and why autoencoders work

How to use transfer learning
Improving model performance using regularization
Optimizing weight initializations
Understand image convolution using predefined and learned kernels
Whether deep learning models are understandable or mysterious black-boxes!
Using gpus for deep learning (much faster than cpus!)
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The theory and math underlying deep learning
How to build artificial neural networks
Architectures of feedforward and convolutional networks
Building models in pytorch
The calculus and code of gradient descent
Fine-tuning deep network models
Learn python from scratch (no prior coding experience necessary)
How and why autoencoders work
How to use transfer learning
Improving model performance using regularization
Optimizing weight initializations
Understand image convolution using predefined and learned kernels
Whether deep learning models are understandable or mysterious black-boxes!
Using gpus for deep learning (much faster than cpus!)
Show more
Show less

Syllabus

Introduction

How to learn from this course

Using Udemy like a pro

Download all course materials

You can download all course code files from the attached zip, or from my github site (same materials).

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Provides a deep dive into deep learning, which gives learners flexible, fundamental, and lasting expertise on the topic, allowing them to learn new trends in the future

Includes an 8+ tutorial, which means that learners do not need to master Python before enrolling and can learn the language from scratch

Explores the theory and math underlying deep learning, including the calculus and code of gradient descent, which is essential for understanding how neural networks learn

Teaches how to build artificial neural networks and fine-tune deep network models, which are core skills for applying deep learning to real-world problems

Explores image convolution using predefined and learned kernels, which is a fundamental technique in convolutional neural networks for image recognition and processing

Leverages Google Colab, an amazing online tool for running Python code, simulations, and heavy computations using Google's cloud services, without needing local installations

Reviews summary

Deep dive into deep learning fundamentals

According to learners, this course offers a deep dive into the theoretical and mathematical foundations of deep learning, distinguishing itself from quick overviews. Students appreciate the clear explanations and the strong emphasis on understanding how and why models work. The inclusion of an 8+ hour Python tutorial is seen as a significant positive, making the course accessible even to coding beginners. Many find the hands-on coding examples and projects using PyTorch particularly useful for solidifying concepts. While the math content is acknowledged as challenging but essential, some mention needing prior basic Python or math familiarity to keep pace.

Can be challenging for true beginners.

"While Python is covered, some basic math or coding intuition is helpful to keep up with the pace."

"The jump from intro Python to complex DL concepts can be steep if you have no prior technical background."

"Some parts moved quite fast, especially in the later math-heavy sections."

Demanding and requires significant effort.

"This is not a quick overview; it's a deep dive that requires commitment and time."

"Be prepared to spend time on the math and coding exercises; it's challenging but rewarding."

"This is not a course for someone who wants a quick overview of deep learning."

Clear and comprehensive explanations.

"The instructor explains complex topics very clearly and provides multiple angles."

"His teaching style is engaging and makes difficult concepts understandable."

"Clear and comprehensible explanations of concepts in deep learning."

"Several distinct explanations of the same ideas, which is a proven technique for learning."

Accessible for Python beginners.

"The included Python tutorial appendix is fantastic! It meant I didn't need to learn Python elsewhere first."

"Great that they offer a Python intro section for those new to coding."

"If you're completely new to Python, go through the 8+ hour coding tutorial appendix."

"You don't need to master Python before enrolling in this course."

Useful hands-on coding practice.

"The coding exercises in PyTorch are practical and reinforce the theoretical concepts effectively."

"Implementing the concepts in PyTorch alongside the lectures made everything click for me."

"LOTS of exercises, projects, code-challenges."

"How are deep learning models actually constructed in Python (using the PyTorch library)?"

Focuses on the math and theory behind DL.

"Excellent coverage of the underlying math and theory, explained clearly."

"The math sections are crucial and explained step-by-step, which is really helpful."

"It provides a strong mathematical foundation that is often skipped in other courses."

"You learn the formulas and mechanisms of deep learning."

Provides a thorough, lasting understanding.

"The course really gives you a deep understanding of the concepts rather than just showing you how to use libraries."

"Finally, a deep learning course that explains the 'why' behind the architectures and math."

"I feel like I truly understand how backpropagation and gradient descent work now, not just the code."

"This course is designed for people who really want to understand how and why deep learning works."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in A deep understanding of deep learning (with Python intro) with these activities:

Review Linear Algebra Fundamentals

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Solidify your understanding of linear algebra, which is crucial for grasping the mathematical underpinnings of deep learning algorithms.

Browse courses on Linear Algebra

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Review matrix operations such as addition, subtraction, and multiplication.
Practice solving systems of linear equations.
Understand the concepts of eigenvalues and eigenvectors.

Brush Up on Python Programming

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Sharpen your Python skills, especially focusing on libraries like NumPy and PyTorch, which are essential for implementing deep learning models.

Browse courses on Python

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Practice writing functions and classes in Python.
Familiarize yourself with NumPy for numerical computations.
Explore PyTorch tensors and basic operations.

Read 'Deep Learning' by Goodfellow, Bengio, and Courville

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Supplement your learning with a comprehensive textbook that covers the theoretical underpinnings of deep learning.

View Deep Learning on Amazon

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Read the chapters relevant to the current course modules.
Work through the examples and exercises in the book.
Compare the book's explanations with the course material.

Four other activities

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Show all seven activities

Implement Gradient Descent from Scratch

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Reinforce your understanding of gradient descent by implementing it from scratch using NumPy.

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Write a Python function to calculate the gradient of a simple function.
Implement the gradient descent algorithm to find the minimum of the function.
Experiment with different learning rates and initial values.

Create a Blog Post on a Deep Learning Topic

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Solidify your understanding by explaining a deep learning concept in your own words through a blog post.

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Choose a specific deep learning topic covered in the course.
Research the topic and gather relevant information.
Write a clear and concise blog post explaining the concept.
Include examples and visualizations to illustrate the concept.

Build a Simple Image Classifier

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Apply your knowledge by building a simple image classifier using PyTorch and a publicly available dataset.

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Choose an image dataset (e.g., CIFAR-10, MNIST).
Preprocess the data and create training and validation sets.
Define a convolutional neural network (CNN) architecture.
Train the model and evaluate its performance.

Skim 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow' by Aurélien Géron

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Broaden your understanding of machine learning and its relationship to deep learning with a practical guide.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read the chapters on neural networks and deep learning.
Experiment with the code examples provided in the book.
Compare the book's approach with the course's approach.

Career center

Learners who complete A deep understanding of deep learning (with Python intro) will develop knowledge and skills that may be useful to these careers:

Deep Learning Engineer

A Deep Learning Engineer specializes in designing, building, and deploying deep learning models. This course offers a deep dive into the field, which is exactly what a deep learning engineer needs. It explicitly focuses on the theory, mathematics, and Python implementation of various deep learning models, which are core components of the role. The course offers a deep understanding of concepts, such as transfer learning, generative modeling, convolutional neural networks, feedforward networks, and generative adversarial networks. It emphasizes how and why deep learning works and when and how to select metaparameters, which is crucial knowledge for a deep learning engineer.

See salaries and explore the career path for Deep Learning Engineer

Machine Learning Engineer

A Machine Learning Engineer designs, develops, and implements machine learning models. This course helps build a deep understanding of deep learning, which is increasingly becoming a standard tool in machine learning, as the course description details. The course provides a deep dive into deep learning, covering the theory, math, and implementation in Python using PyTorch. This hands-on experience is invaluable for a machine learning engineer to understand how deep learning models work in practice. Furthermore, the focus on understanding the fundamental concepts will allow this engineer to adapt to new trends and technologies that emerge.

See salaries and explore the career path for Machine Learning Engineer

Computer Vision Engineer

A computer vision engineer develops systems that can 'see' and interpret images, often using deep learning. This course introduces the architectures of convolutional neural networks, which are extensively used in computer vision tasks. The course also covers the underlying theory and math of deep learning, which helps this engineer be more adept. Through practice problems and explorations of the code, someone working in the field of computer vision can gain a more thorough understanding of these tools.

See salaries and explore the career path for Computer Vision Engineer

Artificial Intelligence Researcher

An Artificial Intelligence Researcher explores new AI techniques, often focusing on deep learning. This course provides a strong theoretical and practical foundation in deep learning, which is essential for any AI researcher. The course covers the underlying math and mechanisms of deep learning, including how to build artificial neural networks using Python. It presents visualizations and several explanations of ideas, which helps an AI researcher develop a deep intuition. Having completed this course, an artificial intelligence researcher will have a basis for innovating in the field.

See salaries and explore the career path for Artificial Intelligence Researcher

Algorithm Developer

An Algorithm Developer designs and implements algorithms for various applications. The course provides a deep dive into the core algorithms underlying deep neural networks, which is a core tool in a algorithm developer's toolkit. The course's approach of starting with the underlying mathematics and then building up to complex models, makes this an ideal course for algorithm developers seeking expertise in deep learning and its related algorithms.

See salaries and explore the career path for Algorithm Developer

Natural Language Processing Engineer

A Natural Language Processing Engineer develops systems that can understand and generate human language. A core skill for this role is the implementation of deep learning models, which this course directly covers. This course offers a detailed introduction to the Python implementation of recurrent neural networks which are applicable towards natural language processing. The course will also provide a strong foundation in the math and theory underlying these models, which is critical towards the success of an engineer in the field.

See salaries and explore the career path for Natural Language Processing Engineer

Research Scientist

A research scientist conducts research in a variety of fields, often using computational tools and techniques. This course may be useful for a research scientist working with data and models. The course offers a deep dive into the theory, math, and implementation of deep learning. The course's focus on Python, PyTorch, and Google Colab also allows for flexible adaptation across many domains of research. Deep learning is increasingly being used across research domains and this course will help build a strong foundation for the researcher.

See salaries and explore the career path for Research Scientist

Robotics Engineer

A robotics engineer designs, builds, and programs robots. The course content on deep learning is increasingly relevant to robotics, as it's used to enable perception, planning, and control, among other tasks. This course provides a deep dive into the math and implementation of neural networks, which forms a core component of modern robotics. Through the course's exercises, a robotics engineer will gain the practical skills necessary for working with neural networks.

See salaries and explore the career path for Robotics Engineer

Data Scientist

A data scientist uses statistical and machine learning techniques to analyze data and extract valuable insights. This course may be useful for a data scientist as it covers the fundamentals of deep learning, an increasingly important technique in data science. The course provides hands-on experience with Python and the PyTorch library, which are common tools for machine learning. Furthermore, the course's focus on the math behind deep learning algorithms allows a data scientist to understand, evaluate, and adapt deep learning models effectively for data analysis projects.

See salaries and explore the career path for Data Scientist

Quantitative Analyst

A quantitative analyst uses advanced mathematical and statistical techniques to develop financial models. This course may be useful for a quantitative analyst by providing a deep dive into the mathematical foundations of deep learning. While deep learning is not the traditional mainstay of quants, the course will provide insight into how neural networks can model complex data, which could be relevant to financial modeling. The course's coverage of PyTorch will also equip them to quickly prototype and iterate on models.

See salaries and explore the career path for Quantitative Analyst

Statistician

A statistician designs and implements statistical models to solve real-world problems. Deep learning is becoming more common in statistics, and this course may be useful by providing a deep look into the mathematical and implementation aspects of these models. This course provides a solid foundation in the math underlying various deep learning algorithms, which may be useful for a statistician interested in the field. It also emphasizes how to select metaparameters and evaluate model performance, which is critical for any statistician.

See salaries and explore the career path for Statistician

Data Analyst

A data analyst collects, analyzes, and interprets data to identify trends and patterns. Deep learning is an increasingly useful technique for data analysis. This course may be useful as it introduces the fundamental concepts behind deep learning, and how these algorithms can be implemented practically. Although the content may be more in-depth than what a data analyst needs for day-to-day work, the material covered in this course is valuable for those who wish to gain a greater understanding of the field.

See salaries and explore the career path for Data Analyst

Software Developer

A software developer designs, develops, and tests software applications. This course may be useful for a software developer due to its coverage of Python and experience using Google Colab. It will help these professionals learn to implement deep learning models. Furthermore, the course focuses on hands-on projects, which provides the practical software development experience for a software developer.

See salaries and explore the career path for Software Developer

Biomedical Engineer

A biomedical engineer applies engineering principles to medical and biological problems. Deep learning is increasingly used in medical imaging, diagnostics, and drug discovery. This course may be useful to biomedical engineers looking for a foundation in deep learning algorithms. Through this course, a biomedical engineer can develop the necessary skills to implement, adapt, and improve deep learning tools for medical applications.

See salaries and explore the career path for Biomedical Engineer

Financial Analyst

A financial analyst analyzes financial data and provides recommendations to manage risk. This course may be useful for a financial analyst by introducing how deep learning algorithms can be used in finance. The course will equip a financial analyst with the technical skills to understand, implement, and adapt the latest technologies in the field. The underlying math and code implementation will provide a hands-on approach towards adapting these tools to finance.

See salaries and explore the career path for Financial Analyst

A deep understanding of deep learning (with Python intro)

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What's inside

Learning objectives

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Reviews summary

Deep dive into deep learning fundamentals

Activities

Career center

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