Introduction to Chatbots and Their Business Value

Introduction to Chatbots and Their Business Value

In today’s world, the majority of businesses have some sort of an e-shop or are e-commerce oriented (meaning: good portion of business can be done online). However, what if your Customer has an issue, and she/he needs to contact the Support, and it is outside of your...
Transfer Learning with TensorFlow 2 – Model Fine Tuning

Transfer Learning with TensorFlow 2 – Model Fine Tuning

In the previous article, we had a chance to explore transfer learning with TensorFlow 2. We used several huge pre-trained models: VGG16, GoogLeNet and ResNet. These architectures are all trained on ImageNet dataset and their weights are stored. We specialized them for...
Business Value of Artificial Intelligence

Business Value of Artificial Intelligence

As you may already know, the amount of data that we create, and store, as human beings has been growing immensely in the last few years. We start having more and more devices that can create, send, store and save data – we can just look at our mobile phones, and how...
Can you be Data Scientist and Software Developer at the same time?

Can you be Data Scientist and Software Developer at the same time?

Coming from the software development background, I am always surprised with the comments from some of the fellow data scientist colleagues that you can’t be data scientist and software developer at the same time. Additionally, this was usually followed by the...
Transformer Series

Transformer Series

In this series of articles we are exploring a special type of sequence-to-sequence models – Transformers. They are big architectures with a lot ot parts and they are used used for language modeling, machine translation, image captioning and text generation....
Deep Learning for ProgrammersLearn how to use software development experience to become deep learning superstar!
  • Why should you care about deep learning?
  • Learn just enough math to be dangerous.
  • Get familiar with Python and TensorFlow.
  • Use familiar paradigms like Object Oriented Programming to understand main Deep Learning concepts.
  • Explore and implement 12 neural network architectures.
  • Solve various real-world problems with neural networks.
  • Learn how to generate images with neural networks.
GO!