Deep Learning Workshop

Connect to your intuition

Feb 14th, 2020

5 Fridays

09:00-14:00

800-1200 NIS

You choose

5 Fridays

09:00-14:00

800-1200 NIS

You choose

Workshop Brief

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 will be held in Hebrew
Lectures and lecturers might be changed

ALL OF THE PROCEEDS ARE BEING DONATED TO THE 3 CHOSEN NONPROFIT ORGANISATIONS

Who Can Join Us

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 ^_^

Our Lecturers

With wide range technological background and almost 18 years of experience in software development in various architecture and technologies, and a big believer in "anything can be solved using code". Today I am working on various machine learning and deep learning projects and specialize in computer vision and NLP on edge devices

Maoz Tamir
CTO - AI Factory

Software Engineering, Data Science with more than 12 years of industry experience delivering architecture services and enterprise software. hands-on specializing in Big Data, unsupervised Learning, Neural Networks & NLP.

Yechiel Amsalem
Senior data scientists at AT&T

Dr. Eyal Gruss is a machine learning researcher and consultant expertizing in image and language processing, with experience in diverse domains, including: medical, financial, cyber, sensors, ads, web, real estate and creative. Eyal Holds a PhD in physics and is a Talpiyot graduate. He is a mentor for Google Launchpad and gives lectures and workshops for both professionals and the general public. Eyal is also a failed entrepreneur, a social activist, a poet and a new-media artist creating interactive installations and computer-generated art.

Dr. Eyal Gruss
AI/Machine Learning/Deep Learning advisor and consultant

Peter has been a neural networks enthusiast for 14 years, previously founded a startup for automatic lip reading from video and Chief Architect at a CyberMDx. Currently runs a blog about AI in visualization and 3D modeling and heads a group of technological experts tackling hard problems for customers: abelians.com

Peter Naftaliev
AI Entrepreneur

A data scientist specialized in video & text analysis, currently working with AiVF Ltd as VP R&D. Wide experience in data management, architecture and reasoning. An AI & DL enthusiast. Cofounder of Give & Tech.

Ron Weiner
VP R&D at AiVF
Co-founder & CEO of Give & Tech

Tamir Nave has 10 years experience in the field of algorithms, focusing on computer vision and deep learning as a developer, team leader, consultant and mentor. Tamir holds a Bsc in math & EE and MA in EE. Tamir is the founder of the collaborative Hebrew blog for AI: ai-blog.co.il

Tamir Nave
Algorithms Expert as a Freelance: Deep learning, Computer Vision Founder of ai-blog

Who we donate to

Schedule

Each workshop day is built from lecturing and hands-on practice.
Don’t forget to bring your laptop!
Practice makes perfect – at the each workshop day you will have lots of home exercise to help you examine all you’ve learned .

14
February
2020, Friday

AI Overview - Maoz Tamir

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.

Machine learning basics - Yechiel Amsalem

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.

21
February
2020, Friday

A shallow introduction to deep learning - Dr. Eyal Gruss

Basic terminology
History and motivation
Computer vision / image processing
Generative adversarial networks (GANs)
Natural language processing
Multimodal methods combining image and text Video, audio, speech Game playing

Build the first Neural Network - Eyal Gruss

Neural Networks
Keras
Data preparation
Training and optimizers
Regularization
Best practices
MNIST and CIFAR10 datasets
Image recognition

Deep Learning for computer vision - Eyal Gruss

Convolutions
Convolutional neural networks
Preprocessing and data augmentation
Common architectures for image recognition
Overview of approaches for other computer vision tasks
Generative algorithms
Autoencoders (optional)
Anomaly detection (optional)

28
February
2020, Friday

Image classification - Maoz Tamir

Using deep neural networks for image classification.
The basic building blocks.
Tips and tricks in image classification .

Object Detection - Maoz Tamir

Object detection basics, classification with localisation.
Different types of Object detection networks – SSD , YOLO.

Object segmentation - Maoz Tamir

Learn how to show this object classifying pixels of the image segmentation.
Different concepts of semantic segmentation vs instance segmentation.

6
March
2020, Friday

3D generation using deep learning - Peter Naftaliev

Technological advances in artificial intelligence allow to take photos of real life objects and automatically create 3D models out of them. This is going to change the way a 3D designer works, allowing for much more efficiency and time saving.

Limitations and possible solutions - Peter Naftaliev

We will see a neural network that takes as input a 2D image and automatically a 3D model, using an encoding-decoding architecture. A ResNet based encoder is trained to encode the image into a z-vector with inherent 3D features and a decoder which is actually a boolean classifier is trained to create a 3D model from the z-vector. The reconstruction can happen in any voxel resolution, without retraining the network. Also we will discuss some of the challenges with 3D modelling and ML, we will present cool implementations of ML in the visualization, texture analysis, 3D modeling and other relevant subjects.

Siamese network as small data solutions - Ron Weiner

How to Learn from Little Data
One-shot classification for a small labeled image dataset.
We’ll also go over the architecture of its inspiration.

13
March
2020, Friday

Introduction to GAN - Tamir Nave

Deep convolutional GAN

Generative Adversarial Networks.

We’re going to use a Deep Convolutional GAN to generate images of pokemons. Architecture and implementation.

Open talk with the lecturers

Summarize & what’s next.

Meeting the Amutot!

Meet the bigger circle, those you donated and helped to!

Deep Learning Workshop – Connect your intuition