"My goal is to work in an AI-related job. However, I believe that the regular online courses, blogs and Youtube tutorials on deep learning are not enough to get me there."
If you can identify with this quote, you are not alone. Let's face it. Although most online courses, YouTube tutorials, and blog articles provide a good foundation for deep learning, they often lack the practical aspect.
After completing one more course or blog article, you may have asked yourself the following questions:
"This was interesting and all, but how can I implement neural networks myself from scratch?"
"I can now implement a neural network, I can train it and it works. But what is the next step? How can I make sure that it works under real-life conditions?"
"Now, that the model works, how can I check whether it is good or not?"
"What if I use a real dataset? How can I properly preprocess it so that I can use it in my project? And what about very large amounts of data?"
"I do not want to use high-level libraries, but understand what's going on under the hood."
"How about making the neural network available to the public so everyone can use it all over the world?"
The available learning material on deep learning and data science is great if you want to understand the theoretical concepts behind AI and neural networks.
However, the reality is that most of the time, it is not enough to get you prepared to use deep learning solutions in the real world.
Although you may understand the mathematics or even know how to implement a deep neural network or a machine learning model, you probably don’t know how to make it work in an industrial environment or how to approach real-life problems with it.
But this is crucial if you pursue a career in AI as a deep-learning engineer or data scientist.
This is where the program “Applied Deep learning for Predictive Analytics” comes into play.
I am very glad to have your attention. Please let me introduce it to you...
"Applied Deep Learning for Predictive Analytics" is an advanced deep learning education and one-on-one mentoring program developed in collaboration with experts in the field of deep learning and data science to help you to start a career in the field of artificial intelligence.
This program is for those who aspire to work in an AI-related job. We want to help you to land your first job in this interesting and thrilling field and of course, prepare you for it.
The typical work of a deep learning engineer requires some basic skills that you need to have. Teaching you these skills is what this mentoring program is all about.
Most courses out there lack one simple thing, which is the individual support of the students. This is why a major focus of this education program is to give you that individual support along the way.
You will be in direct contact with someone more experienced in this field who was in exactly the same position a few years ago as you are now.
You can count on immediate support regarding any topic - Be it questions, issues with your code, reviews of your projects or others.
To summarize, in this program you are going to...
Great, well structured program, ideal for those who want to work on real deep learning projects. Exercises and code examples are also very helpful to solidify the knowledge. I recommend this course to anyone who wants to work in the field of Deep Learning professionally.
Johannes Lueken, CEO MiiMedia
This program goes beyond neural networks. Instead, it covers topics that are crucial for a professional career in Deep Learning. Just to name a few: Best practices for implementing neural network models, data-preprocessing, efficiency, GPU-Training, model evaluation, deployment of the models to production etc.
Vitali Rudi, Industrial Engineer
The best thing for me personally was to see the different steps of a typical deep learning project and how to approach these steps in practice. I'm now more confident about taking part in a project myself and solving problems with neural networks.
Adile Kilic, Economical Engineer
The theoretical concepts that you will learn in this program will not remain just theory.
You will have the opportunity to work on up to 4 projects in which you will put all your new knowledge about Deep Learning into practice and solve real business problems.
These business problems are set in the area of Predictive Analytics, a subfield of Business Intelligence.
For each project, you will implement Deep Neural Network Models covering the following areas:
We will assist you directly with the programming and debugging of your code, review your work and give you feedback accordingly, and of course helping you with any other problems that arise.
The work on these projects will be happening exclusively through GitHub. Besides, with GitHub you will learn the go-to hosting platform for code and project collaboration in the field of Deep Learning.
In addition to the obvious practical aspect, the projects of this course serve a second purpose.
We want to give you the feeling of working in a real Deep Learning project and experiencing the typical life-cycle of a Deep Learning / Data Science project.
During the practical part of the course we will guide you through each step of this lifecycle, from data exploration over model prototyping to deploying the final Neural Network Models to production. In this case, making the Neural Networks available as Microservice Applications through Amazon Web Services.
This program is also a great oppurtunity to build and / or extend your Deep Learning project portfolio. We strongly recommend that you present the projects you will work on in this program to the public through GitHub.
In this way, you can demonstrate and verify your newly acquired deep learning skills to your potential employer and show that you are capable of solving various Business Problems with Deep Learning.
The course is very well structured, mathematical forms are exact and explained thoroughly. Very practical. Many clear code examples. This course has really given me what I was looking for - the practical application of deep neural networks with TensorFlow and Python.
Andre Brant, Biomedical Physicist
Great personal Support. Very detailed explanations. The step-by-step code along was really helpful for understanding the concepts and codes.
Waldemar Herner, Mechanical Engineer
Very good and detailed instructions with relevant code examples after each lecture. Depth of coverage is amazing.
Mario Ennens, Project Manager in Engineering
You don't have to learn and implement the projects all by yourself. After the enrollment you will receive your personal access to the official Slack group of the academy. Slack is a team collaboration and project management tool, where you can discuss any course related topics and ask questions.
You can count on the immediate support of the instructor and your fellow students. Or even better: pair-up with others and work on the projects togheter.
Deep Learning is a very young and exciting field and the best approach to what we can call genuine Artificial Intelligence. In the coming years this technology will have a significant impact on society, industry and our our day to day lives, and change them for the better.
On the one hand we have these great opportunities, on the other hand we may not be able to take advantage of these opportunities. This is because the number of people who can actually implement Deep Learning Solutions in practice is very limited.
This shortage of qualified people with hands-on, practical knowledge slows down the progress of AI. At the same time companies skyrocket the salaries to acquire the people with the right knowledge:
“There are about 300,000 AI practitioners and researchers worldwide, but millions of roles are available for people with these qualifications.” A report by the Chinese technology company Tecent
“Shortage of AI-trained engineers and developers is persisting. Lack of skilled people is the number one bottleneck in adoption of AI solutions.” "How Companies Are Putting AI to Work Through Deep Learning", O’Reilly Report Paper, 2018
“While the average salary for a programmer is around $100,000 to $150,000, to make the big money you want to be an AI engineer with an avg. salary of $169,930 per year.” Indeed.com, 2018
In this course you will learn and use the latest technologies that every Deep Learning Engineer should know to build, test and deliver Deep Learning solutions.
Although my background is in physics, early on I developed a passion for computer science, AI and especially deep learning. Today, I spend most of my time as a deep learning engineer with emphasis on computer vision and sequential models. Some of the deep neural network models I've implemented over the years include:
When I began to study Deep Learning back in the day I took some excellent online courses on several platforms like Udemy, Udacity and Coursera.
Although they provided me with great knowledge about the subject, all courses lacked one particular last step I was looking for:
How to apply my knowledge in a real production environment.
I was confident in building Deep Learning models locally, but facing the real world and do the same for customers or employers is a whole different story. In the end, I acquired the necessary skills the hard way by fighting through countless bite-sized pieces of information.
I want to provide a shortcut in learning for those who want to work in this amazing field professionally.
The course starts now and never ends! It is a completely self-paced online course - you decide when you start and when you finish.
How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.
We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 30 days and we will give you a full refund.