College Level Neural Nets [I] - Basic Nets: Math & Practice! from Udemy

Deep Learning is surely one of the hottest topics nowadays, with a tremendous amount of practical applications in many many fields.Those applications include, without being limited to, image classification, object detection, action recognition in videos, motion synthesis, machine translation, self-driving cars, speech recognition, speech and video generation, natural language processing and understanding, robotics, and many many more.

Now you might be wondering :

There is a very large number of courses well-explaining deep learning, why should I prefer this specific course over them ?

The answer is : You shouldn't . Most of the other courses heavily focus on "Programming" deep learning applications as fast as possible, without giving detailed explanations on the underlying mathematical foundations that the field of deep learning was built upon. And this is exactly the gap that my course is designed to cover. It is designed to be used hand in hand with other programming courses, not to replace them.

Since this series is heavily mathematical, I will refer many many times during my explanations to sections from my own college level linear algebra course. In general, being quite familiar with linear algebra is a real prerequisite for this course.

Please have a look at the course syllables, and remember : This is only part (I) of the deep learning series.

What's inside

Learning objectives

Step by step conceptual introduction for neural networks and deep learning [even if you are a beginner]
Understanding the basic perceptron[neuron] conceptually, graphically, and mathematically - perceptron convergence theorem proof
Mathematical derivations for deep learning modules
Step-by-step derivation of backpropagation algorithm
Vectorization of backpropagation

Different performance metrics like performance - recall - f1 score - roc & auc
Mathematical derivation of cross-entropy cost function
Mathematical derivation of back-propagation through batch-normalization
Different solved examples on various topics

Step by step conceptual introduction for neural networks and deep learning [even if you are a beginner]
Understanding the basic perceptron[neuron] conceptually, graphically, and mathematically - perceptron convergence theorem proof
Mathematical derivations for deep learning modules
Step-by-step derivation of backpropagation algorithm
Vectorization of backpropagation
Different performance metrics like performance - recall - f1 score - roc & auc
Mathematical derivation of cross-entropy cost function
Mathematical derivation of back-propagation through batch-normalization
Different solved examples on various topics

Syllabus

Introduction To Machine Learning

Promo Video

The Linear Perceptron

Introduction To The Classification Problem

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Focuses on the mathematical derivations behind deep learning modules, which is essential for those aiming to deeply understand and potentially innovate in the field

Assumes familiarity with linear algebra, which is a prerequisite for understanding the mathematical concepts presented in the course

Provides a step-by-step derivation of the backpropagation algorithm, which is a core component of training neural networks

Includes a mathematical derivation of the cross-entropy cost function, which is a fundamental concept in training neural networks for classification tasks

Presents a mathematical derivation of back-propagation through batch normalization, which is a technique used to improve the training of deep neural networks

Explains different performance metrics like precision, recall, F1 score, ROC, and AUC, which are essential for evaluating the performance of machine learning models

Reviews summary

Mathematical foundations of basic neural nets

According to learners, this course provides a deep dive (positive) into the mathematical foundations (positive) of basic neural networks, explicitly focusing on the "why" rather than the "how to code". Students particularly appreciate the detailed derivations (positive), especially for the backpropagation algorithm (positive), finding that it helps solidify their understanding of the core mechanics. However, prospective students should be aware that this course contains very little practical coding (warning) and is heavily theoretical (neutral). Learners recommend having a strong background in linear algebra (warning) as it is a necessary prerequisite. It is considered an excellent foundational course (positive) for those who want to truly understand the math behind neural nets, complementing other programming-focused resources.

Instructor's teaching style is generally clear.

"The instructor is very knowledgeable and explains complex concepts clearly."

"His step-by-step approach to derivations is easy to follow, provided you have the math background."

"Some parts felt a bit fast-paced, but overall, the explanations were detailed and thorough."

Provides a strong theoretical base.

"This course is an excellent foundation for understanding the fundamental math before diving into more advanced topics or frameworks."

"It gives you the theoretical understanding needed to make sense of why neural networks work the way they do."

"A perfect complement to programming-focused courses, giving you the 'why' to go along with the 'how'."

Excellent, step-by-step backprop explanation.

"The derivation of backpropagation is broken down into such clear, manageable steps. I finally understand how it works mathematically."

"I've seen backprop explained before, but the step-by-step mathematical derivation here is by far the best I've encountered."

"Understanding the math behind backprop was my main goal, and this course delivered completely on that front."

Covers mathematical derivations in detail.

"This course provides a very detailed mathematical breakdown of neural networks. It goes deep into the derivations, which is exactly what I needed."

"If you want to understand the math behind how neural nets work, this is the course for you. The instructor walks through proofs and equations step-by-step."

"Finally, a course that doesn't shy away from the math! Understanding the derivations makes the concepts much clearer."

"The level of mathematical detail is exceptional. It's college-level indeed and explains the 'under the hood' aspects very well."

Linear algebra prerequisite is crucial.

"You absolutely need a solid grasp of linear algebra to follow this course. Without it, you will struggle significantly."

"The course is labeled 'college level' for a reason. Make sure your math foundation, especially linear algebra, is strong."

"Came into this with weak math skills and found it very challenging. This course is not for beginners without the prerequisites."

Focus is heavily theoretical, minimal coding.

"As the course description states, there is virtually no coding. This is purely theoretical and mathematical. Be prepared for that."

"If you're looking for practical coding exercises or how to use libraries, this isn't it. It's all math and theory."

"I found myself needing to supplement with other resources for the programming aspect, as this course is strictly the mathematical core."

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 College Level Neural Nets [I] - Basic Nets: Math & Practice! with these activities:

Review "Linear Algebra and Its Applications" by Gilbert Strang

Show steps

Solidify your understanding of linear algebra concepts, which are essential for understanding the mathematical underpinnings of neural networks.

View Linear Algebra for Everyone on Amazon

Show steps

Read the chapters on vector spaces and linear transformations.
Work through the example problems in the book.
Complete the exercises at the end of each chapter.

Review Calculus Fundamentals

Show steps

Reinforce your calculus knowledge, particularly derivatives and the chain rule, which are fundamental to understanding backpropagation.

Browse courses on Partial Derivatives

Show steps

Review the definitions of derivatives and integrals.
Practice taking derivatives of various functions.
Study the chain rule and its applications.

Implement Perceptron Learning Rule

Show steps

Gain hands-on experience by implementing the perceptron learning rule from scratch, solidifying your understanding of its mechanics.

Show steps

Write a function to initialize perceptron weights.
Implement the perceptron prediction function.
Implement the perceptron learning rule update.
Test your implementation on simple datasets.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Help Others in Online Forums

Show steps

Reinforce your understanding by helping other students in online forums, answering their questions and explaining concepts.

Show steps

Find online forums related to neural networks and deep learning.
Browse the forums for questions you can answer.
Provide clear and concise explanations.

Create a Visual Explanation of Backpropagation

Show steps

Deepen your understanding of backpropagation by creating a visual explanation, such as a diagram or animation, that illustrates the flow of gradients through a neural network.

Show steps

Choose a tool for creating visuals (e.g., PowerPoint, animation software).
Outline the key steps of backpropagation.
Create visuals to represent each step.
Add annotations and explanations to your visuals.

Build a Simple Neural Network from Scratch

Show steps

Solidify your knowledge by building a basic neural network from scratch using Python and NumPy, without relying on deep learning frameworks.

Show steps

Implement the forward pass for a single layer.
Implement the backward pass (backpropagation).
Train your network on a simple classification task.
Evaluate the performance of your network.

Review "Deep Learning" by Goodfellow, Bengio, and Courville

Show steps

Expand your knowledge of deep learning concepts and techniques beyond the scope of this introductory course.

View Deep Learning on Amazon

Show steps

Read the chapters on feedforward networks and backpropagation.
Explore the chapters on regularization and optimization.
Refer to the book for more in-depth explanations of specific topics.

Career center

Learners who complete College Level Neural Nets [I] - Basic Nets: Math & Practice! will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A Machine Learning Engineer builds and deploys machine learning models. This role requires a strong understanding of the mathematical foundations of neural networks. The College Level Neural Nets course, with its deep dive into the basic perceptron, backpropagation, and gradient descent, ensures a solid theoretical understanding. It is also particularly suited to this role because it focuses on the mathematical derivations of deep learning modules and the vectorization of backpropagation. Understanding the underlying math is crucial for troubleshooting and optimizing models, making this course an excellent starting point. Understanding how to derive different performance metrics such as recall and F1 score also fits in with the data analysis that a Machine Learning Engineer performs.

See salaries and explore the career path for Machine Learning Engineer

Deep Learning Researcher

A Deep Learning Researcher investigates new deep learning models and algorithms. To succeed as a Deep Learning Researcher typically requires an advanced degree. The College Level Neural Nets course provides a strong foundation in the mathematical underpinnings of neural networks, including the derivation of backpropagation and the analysis of performance metrics. The course's emphasis on mathematical rigor, especially its detailed explanations of linear algebra's role in deep learning, makes it invaluable. A Deep Learning Researcher benefits from the course's detailed exploration of the mathematical principles behind neural networks, going beyond just programming applications.

See salaries and explore the career path for Deep Learning Researcher

Artificial Intelligence Specialist

An Artificial Intelligence Specialist designs and implements AI solutions. This role requires a strong understanding of neural networks and deep learning. This College Level Neural Nets course offers a comprehensive introduction to these topics, covering everything from the basic perceptron to the back-propagation algorithm. The course's emphasis on mathematical derivations and conceptual understanding, rather than just programming, makes it particularly valuable. By teaching the mathematics behind deep learning this course helps you understand the fundamental principles of Artificial Intelligence Specialist work.

See salaries and explore the career path for Artificial Intelligence Specialist

Data Scientist

A Data Scientist analyzes large datasets to extract meaningful insights; often using machine learning models. The College Level Neural Nets course provides a solid foundation in the mathematical principles behind these models, particularly neural networks. The course covers essential topics, such as the back-propagation algorithm, regularization, and model performance metrics. For a Data Scientist, this course may be useful because it emphasizes the importance of understanding the math behind the models. The discussion of precision, recall, and F1 score directly applies to the data analysis that a Data Scientist performs.

See salaries and explore the career path for Data Scientist

Computer Vision Engineer

A Computer Vision Engineer develops algorithms that allow computers to 'see' and interpret images. Neural networks are at the heart of many computer vision applications. The College Level Neural Nets course teaches the mathematical foundations necessary to building vision algorithms. By learning the math behind backpropagation, regularization, and various performance metrics, one will better understand how to build, train, and evaluate computer vision models. The exploration of image classification, object detection, and action recognition in videos gives a Computer Vision Engineer a helpful head start.

See salaries and explore the career path for Computer Vision Engineer

Robotics Engineer

A Robotics Engineer designs, builds, and programs robots. Deep learning is increasingly used in robotics for tasks such as perception, navigation, and control. The College Level Neural Nets course provides a strong foundation in the mathematical principles of neural networks, which are essential for understanding and implementing these deep learning applications. It covers topics such as the back-propagation algorithm, regularization, and model performance metrics. By learning the underlying mathematics rather than just programming, this theoretical understanding will allow a Robotics Engineer to custom-build and optimize robotic systems.

See salaries and explore the career path for Robotics Engineer

Natural Language Processing Engineer

A Natural Language Processing Engineer develops algorithms to enable computers to understand and process human language. This role requires a strong understanding of neural networks and deep learning. The College Level Neural Nets course offers a solid introduction to these topics, covering everything from the basic perceptron to the back-propagation algorithm. The course’s focus on mathematical derivations and conceptual understanding makes it particularly valuable. The discussions relating to video and speech recognition help Natural Language Enginners understand how language algorithms are related to other AI solutions.

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

AI Game Developer

An AI Game Developer uses artificial intelligence techniques to create more intelligent and engaging game experiences. This often involves implementing neural networks for character behavior, pathfinding, and other game mechanics. The College Level Neural Nets course can be useful by providing a foundation in the mathematical principles behind neural networks. The course covers essential topics, such as the back-propagation algorithm and regularization. The mention of motion synthesis and action recognition within the course is particularly related to the kind of automation that exists in the gaming world.

See salaries and explore the career path for AI Game Developer

Quantitative Analyst

A Quantitative Analyst, often working in the finance industry, develops and implements mathematical models for pricing securities, managing risk, and making trading decisions. While not directly focused on finance, the College Level Neural Nets course provides a strong foundation in the mathematical principles and algorithms that underlie many machine learning models, some of which may be applied to financial data. The emphasis on mathematical rigor and the focus on topics such as regularization, cross-validation, and performance metrics can be useful for anyone working with quantitative models, including a Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst

Software Engineer

A Software Engineer designs, develops, and tests software applications. While not always directly related to neural networks, the College Level Neural Nets course can be useful by providing a deeper understanding of the mathematical principles behind machine learning algorithms. This course helps you to better understand the tools and libraries they use. The course's emphasis on mathematical derivations and conceptual understanding may prove useful. The course is particularly applicable to a Software Engineer working on AI powered software.

See salaries and explore the career path for Software Engineer

Data Analyst

A Data Analyst collects, cleans, and analyzes data to identify trends and insights. While this role typically involves more statistical analysis than deep learning, the College Level Neural Nets course may be useful by providing a foundational understanding of machine learning concepts. The course covers topics such as model performance metrics, which can be helpful for evaluating the results of machine learning models used in data analysis. The course gives instruction and mathematical background on statistical analysis.

See salaries and explore the career path for Data Analyst

Business Intelligence Analyst

A Business Intelligence Analyst analyzes business data to identify trends and insights that can help improve decision-making. While this role is primarily focused on data visualization and reporting, the College Level Neural Nets course may be useful. This is because it provides a foundational understanding of machine learning concepts. The course covers topics such as model performance metrics, which can be helpful for understanding the results of machine learning models used in business analytics. The course is particularly useful for the evaluation of data.

See salaries and explore the career path for Business Intelligence Analyst

Web Developer

A Web Developer builds and maintains websites. While this role typically doesn't involve deep learning, the College Level Neural Nets course may be useful by providing a basic understanding of machine learning concepts. This knowledge can be helpful when integrating AI-powered features into websites. An example of an AI-powered feature includes chatbots or recommendation systems. The course is particularly applicable when deep learning is used in the architecture of the website.

See salaries and explore the career path for Web Developer

Database Administrator

A Database Administrator manages and maintains databases. The College Level Neural Nets course can be useful by providing a basic understanding of machine learning concepts. The content is helpful when working with databases that store data used for machine learning. Although deep learning is not directly involved in data administration this course may provide instruction on the mathematics behind storage and recall of data.

See salaries and explore the career path for Database Administrator

Technical Writer

A Technical Writer creates documentation for software and hardware products. Though not directly related, understanding the underlying principles discussed in the College Level Neural Nets course may be useful. This is particularly true if the documentation involves AI or machine learning. A Technical Writer benefits from knowledge of neural nets when they are required to write about it.

See salaries and explore the career path for Technical Writer

College Level Neural Nets [I] - Basic Nets

Math & Practice!

What's inside

Learning objectives

Syllabus

Traffic lights

Save this course

Reviews summary

Mathematical foundations of basic neural nets

Activities

Career center

Reading list

Share

Similar courses