Have you heard of these technologies—but have no idea how they work?
Deep learning can sound incredibly complicated. For example to understand CNNs you need to at least understand this sentence:
"This architecture has some building blocks that you already know, such as fully connected layers and sigmoid activation functions, but it also introduces two new building blocks: convolutional layers and pooling layers." From Hands-on Machine Learning and Scikit-Learn, Keras & Tensorflow
And that requires knowing what fully connected layers, sigmoid activation functions, convolutional layers and pooling layers are.
These all sound like advanced and complicated topics but if you only have a good understanding of the basics of neural networks, they will be yesterday's news for you.
Deep Learning 101 will teach you all the essentials of deep learning without wasting your time.
After months of research and combing through many online courses, books and blog posts on the topic, I brought together a comprehensive and distilled guide to deep neural networks.
By the end of this course, you will be a confident deep learning practitioner, who knows:
This fundamental knowledge will open up the gates to more advanced learning for you. You will not feel confused or overwhelmed when reading about a new deep learning algorithm.
You will learn how to implement everything you learn and gain hands-on experience by trying it out yourself. Assignments will make sure you have permanent take-aways.
The main feature of the course is the video explanation of everything about deep learning. We will start from simple topics such as what neural networks are and how they learn and build our knowledge up to RNNs and CNNs.
All of the concepts we introduce in this course are things we can implement using Keras and Python. After each theoretical lesson, there will be videos showing you how to implement each of the concepts we introduced in the theoretical section.
At important milestones of your learning, there will be video walkthroughs of exercises. These include importing and preparing datasets, building and compiling neural networks, applying regularization to networks, evaluating and comparing networks and more.
After the exercises, you will be given assignments to interact with the code from the exercises in order to understand some of the key concepts better.
You will have access to all the material used while teaching. The course slides will be shared with each lesson for you to keep a copy of.
At the end of each chapter, you will have a couple of questions to answer. The answers to these questions will also be shared with you. Even though all of the topics discussed during the lessons are important, these questions will emphasize the must-know points from each lesson.
Each lesson comes with a PDF of summary notes. These notes will include key information that was introduced during the classes. You can download and keep these notes to use as a reference during the exercises or in the future.
At the end of the course, we will do a final project using a pre-trained model from the internet to solve an image recognition task. This project will help you understand how deep learning is used in the industry.
If you have any questions or comments, you can leave a comment on lesson videos or exercises and your instructor will get back to you.
You will have access to all the code developed in the course. You can use this repository to follow coding videos along. You can also use the repository as a reference while doing your assignments. Or in the future to remember how a certain technique is implemented.
“The course is great for beginners who already have some concepts on how to analyze data.
Deep Learning 101 will launch on 18 December 2021.
Leave your email below to be notified.
On this page, I will post updates on the progress of the course. I will also share these updates through email to those interested. If you'd like to get these updates and more useful *free* content from me, sign up below:
Introduction to Deep Learning
Building blocks of Deep Learning
Exercise - Let's build a simple Neural Network
Hyperparameters of NNs
Unstable gradients problem
Exercise - Setting up NNs
Model diagnosis and making improvements
Exercise - Improving the model
Introduction to CNNs and RNNs
Final Project - Building a web app
I will be your instructor on this course.
Over the years, as a data scientist, I worked at big companies like IBM and also small niche companies that are trying to make the world a better place. Now, I teach what I learned about data science online.
As a computer science student, I never got a formal education on deep learning. Back when I was in university, deep learning was not even a common topic that was taught at school. Still, my thesis topic was a robot choosing its own hand and arm gestures to accompany its speech using RNNs. Then my journey into the deep realm of deep learning began.
It took me years of self-study, trial and error, many books and online courses to come to the point where I am today. My goal is to bring you to that same point without all the effort and time it took me.
I had to learn deep learning from many resources that teach things in the most formal way possible. Burying its students with endless equations, math formulas and theoretical, abstract concepts before being able to understand the bigger picture logic. If you take a practical approach though it becomes much easier to learn deep learning. That's what we're going to do in this course.
This is an online course you will not abandon after a couple of days. The material is advanced but the delivery will help you feel progress every step of the way.
Looking forward to seeing you there!
After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.
You need to have an understanding of basic data science concepts such as training a model, testing a model, evaluation metrics, classification vs. regression, supervised vs. unsupervised problems.
Everything else, we will cover in this course.
The difference between Deep Learning 101 and other online courses is the delivery and the teaching approach. In this course, the goal is to bring you from very little knowledge of data science to having a solid grasp of all essential deep learning topics. At the end of this course, you will have a full understanding of all the building blocks and this will enable you to understand more complex topics and algorithms that were built using these building blocks.
We will not overload you with information that will not stay with you after a couple of days. The goal is not to throw every little detail at you but rather to give you distilled information that will enable you to apply deep learning in whichever way you need it.
The lessons are not in a university class format like in many other online courses. For every new concept, we start from a high-level working logic and only if necessary go into the depths of theory.
Of course, there are many free resources online about data science and deep learning. The main advantage of taking Deep Learning 101 is to have someone to collect, distil and organize the information for you in a way that is ready to be consumed right away.
Learning by yourself costs time and extra effort to find just the right information that is not outdated. And it can become an overwhelming and tiresome exercise.
I have been through that already and now, I offer you a shortcut.
Yes. The call must be planned within three months of purchase. I will send you a link with which you can schedule a call with me right after you enroll in the course.
The exercises are executed in Python in a Jupyter notebook using Keras with a Tensorflow backend. All of these technologies will be introduced during the course but there is no Python or Jupyter notebooks tutorial inside the course.
You do not need to have any prior math knowledge to start this course.
If you take your time, follow all lessons and complete the assignments the course will take you around 2 to 3 weeks.
The course is OS agnostic. We will be setting up and executing the exercises in MacOS but as long as you have access to Jupyter Notebooks, that should not be a problem. We will also introduce a way to follow the exercises in case you have a slow computer that cannot do the computations locally.
Yes. This course comes with a 30-day money-back guarantee. If, for any reason, the course does not meet your expectations and you would like a refund, send me an email at email@example.com and I will arrange your refund.