Video Course: Deep Learning 101 with Python and Keras

$119
Enroll now!
30-Day Money-Back Guarantee

This course includes:

6 hours on-demand video
13 modules
11 quizzes and assignments
7 lesson summary PDFs
3 coding exercises
A final project
Full lifetime access
Access on mobile
Certificate of completion
Access to instructor

Deep Learning Doesn’t Have to Be Confusing

Learn the essentials of Deep Learning to apply it in your work confidently.

Have you heard of these technologies—but have no idea how they work?

  • GPT-3
  • Transformers
  • CNNs
  • LSTMs

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:

  • how to set up a deep neural network from scratch,
  • how to train it and
  • how to improve its performance.

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.

Here is how we're going to do that

Explanation of key concepts

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.

Implementation of each technique

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.

Guided exercises

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.

Programming assignments

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.

Lesson slides

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.

End-of-chapter quizzes

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.

Lesson summary notes

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.

Final project

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.

Access to the instructor through comments

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.

Course repository

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.

This course is for you if:

  • you want to learn deep learning efficiently
  • you are willing to put in the work
  • you want to be able to follow the latest developments in AI
  • you want to improve the work you do by experimenting with deep learning techniques
  • you are interested in practical uses of deep learning

This course is not for you if:

  • you don't want to put in the work to get the fundamentals straight
  • you want to learn all the math and theory behind deep learning algorithms

“The course is great for beginners who already have some concepts on how to analyze data.

  1. The structure and explanations are clear,
  2. The teaching style is really different from those boring, push-the-info, be over the top approaches,
  3. Material is nicely worked out.”
Zoltán Szlávik
Head of Data Science at XITE

Coming soon!

Deep Learning 101 will launch on 18 December 2021.

Leave your email below to be notified.

Get updates!

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:

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Enroll now!

Regular edition

  • 11 modules explaining DL concepts
  • Bonus module on CNNs and RNNS
  • Final project: making a deep learning web app
  • Assignment, quizzes and hands-on exercises
  • Lesson summary notes
  • Access to course code repository
  • Access to your instructor through comments
  • Hyperparameters cheat sheet
  • One 60-minute video call to answer your questions one-on-one
$119
Buy now
Sales tax or VAT might apply.

Premium edition

  • 11 modules explaining DL concepts
  • Bonus module on CNNs and RNNS
  • Final project: making a deep learning web app
  • Assignment, quizzes and hands-on exercises
  • Lesson summary notes
  • Access to course code repository
  • Access to your instructor through comments
  • Hyperparameters cheat sheet
  • One 60-minute video call to answer your questions one-on-one
$229
Buy now
Sales tax or VAT might apply.

30-day money-back guarantee

This course comes with a 30-day money-back guarantee. If you’re not happy with the course for any reason, I'll refund your payment in full.

Here's what we will cover in this course

Introduction to Deep Learning

Building blocks of Deep Learning

Exercise - Let's build a simple Neural Network

Hyperparameters of Neural Networks

Over/underfitting problem and solutions

Unstable Gradients

Computational time

Exercise - Setting up neural networks

Model diagnosis and making improvements

Hyperparameter tuning

Exercise - Improving the model

Bonus: Intro to CNNs and RNNs

Final Project - Building a deep learning web app

Module 1

Introduction to Deep Learning

  • Quick overview of deep learning
  • What are neural networks
  • Common DL techniques

Module 2

Building blocks of Deep Learning

  • Weights and biases
  • Activation function
  • Vectorization of calculations
  • Forward propagation
  • Backward propagation
  • Gradient descent

Module 3

Exercise - Let's build a simple Neural Network

  • Setting up a virtual environment
  • Finding, importing and preparing data
  • Building a network
  • Model evaluation

Module 4

Hyperparameters of NNs

  • Overview of NN working principles
  • Number of neurons
  • Number of hidden layers
  • Loss functions and optimizers
  • Activation functions
  • Batch size
  • Regularization
  • and more...

Module 5

Over/underfitting problem

  • What is overfitting?
  • L1 and L2 regularization
  • Dropout regularization
  • Data augmentation
  • Early-stopping

Module 6

Unstable gradients problem

  • What is vanishing/exploding gradients?
  • Weight initialization techniques
  • Non-saturating activation functions
  • Batch normalization
  • Gradient clipping

Module 7

Computational time

  • Data normalization
  • Batching
  • Optimization algorithms
  • Network pruning
  • Learning rate scheduling

Module 8

Exercise - Setting up NNs

  • Initializing the model
  • Dealing with underfitting
  • Dealing with overfitting
  • Experimenting with changing hyperparameters

Module 9

Model diagnosis and making improvements

  • Model evaluation metrics
  • Levels of performance
  • Determining the performance level to aspire to
  • Making a plan for improvements

Module 10

Hyperparameter tuning

  • Why is this a problem?
  • Common ways to tune hyperparameters
  • Advanced hyperparameter tuning algorithms

Module 11

Exercise - Improving the model

  • Detecting the performance gap
  • Setting up improvements

Bonus

Introduction to CNNs and RNNs

  • What are CNNs / RNNs
  • How do they work
  • Why are they better than feed forward NNs
  • How can we use them

Final Project (arrives in 2022)

Final Project - Building a web app

  • Where to find pre-made models
  • Integrating pre-made models
  • Making a web app for our project

Take a look at a full table of contents and watch preview lessons here.

Hi, I am Mısra...

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!

“I totally recommend this course. I think understanding deep learning is a bit of a challenge, but  Mısra has a very friendly and personal way to walk you through concepts; it almost feels like your best friend is teaching you. It is a a class up to date with all the necessary information to start your own project right away.”
Erick Velázquez Godínez
NLP specialist

Frequently Asked Questions

Q: How long do I have access to the course?

After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.

Q: What are the requirements for taking this course?

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.

Q: Why should I prefer this course over other online courses?

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.

Q: Why should I take this course when there are so many free online resources?

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.

Q: Is there a time limit on the one-on-one call in the premium version?

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.

Q: Which programming language is used in exercises?

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.

Q: Do I need excellent math skills to join this course?

You do not need to have any prior math knowledge to start this course.

Q: How long will this course take to finish?

If you take your time, follow all lessons and complete the assignments the course will take you around 2 to 3 weeks.

Q: Which OS (operating system) is the course suitable for?

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.

Q: Can I get a refund if the course does not meet my expectations?

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 misra@soyouwanttobeadatascientist.com and I will arrange your refund.

‍

Enroll now!

Regular edition

  • 11 modules explaining DL concepts
  • Bonus module on CNNs and RNNS
  • Final project: making a deep learning web app
  • Assignment, quizzes and hands-on exercises
  • Lesson summary notes
  • Access to course code repository
  • Access to your instructor through comments
  • Hyperparameters cheat sheet
  • One 60-minute video call to answer your questions one-on-one
$119
Buy now
Sales tax or VAT might apply.

Premium edition

  • 11 modules explaining DL concepts
  • Bonus module on CNNs and RNNS
  • Final project: making a deep learning web app
  • Assignment, quizzes and hands-on exercises
  • Lesson summary notes
  • Access to course code repository
  • Access to your instructor through comments
  • Hyperparameters cheat sheet
  • One 60-minute video call to answer your questions one-on-one
$229
Buy now
Sales tax or VAT might apply.
Contact email: misra@soyouwanttobeadatascientist.com
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Tax ID: NL003288205B55
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