Lots of buzzwords, lots of unicorns, but we will make it practical and lead you in your first steps to the magical place of data science, specifically, deep learning. We will talk a bit about the theory behind it, but mostly, we will demonstrate the variety of common techniques and usages. You will acquire the fundamental knowledge and tools to implement deep learning architecture by yourself.
The workshop is for anyone who has at least 2 years of experience with programming (preferably some knowledge of python) , logic orientation, and an outstanding will to learn.
In the workshop, we will use python, sklearn, tensorflow and keras.
This is a complex topic, you should work hard and be very focused during those weeks ^_^
Each workshop day is built from live lecturing and hands-on practice.
We will be available over slack to answer anything for the rest of the week.
Practice makes perfect – at the each workshop day you will have lots of home exercise to help you examine all you’ve learned.
During the workshop you will meet the “Amutot” we donate to in this workshop.
What differs AI from other kind of algorithms? What’s the relation between AI and Machine Learning? How did it all start and what is Deep Learning? In this lecture we will cover the buzz words, history and general notions around the topic of AI from a technical perspective.
We’ll gain a deep intuitive understanding of the machine learning field. Start with covering the main concepts and tools of machine learning, both theoretically and practically.
This is the fundamentals for the understanding of how to use Deep Neural Networks.
Among the main topics: what is the prediction pipeline, feature engineering, linear & logistic regression, learning tasks and measurement metrics.
Basic terminology, history and motivation.
What is “Neural Networks”, common frameworks (Keras).
Main blocks in the process and best practices:
Training and optimizers
The basic building blocks, tips and tricks in image classification with deep learning.
Convolutional neural networks
Preprocessing and data augmentation
Common architectures for image recognition
Overview of approaches for other computer vision tasks
We will go through the differentiators of processing time series data.
Which methods of deep learning can be useful for it, RNNs, CNNs etc.
How can we create useful baseline initialisation and transfer learning.
Training vs inference mode – there are multiple configurations worth knowing.