Feb 14th, 2020
09:00-14:00
You choose
Tel- Aviv
Feb 14th, 2020
09:00-14:00
You choose
Tel- Aviv
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 ^_^
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
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.
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.
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
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.
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
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 .
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
Computer vision / image processing
Generative adversarial networks (GANs)
Natural language processing
Multimodal methods combining image and text Video, audio, speech Game playing
Neural Networks
Keras
Data preparation
Training and optimizers
Regularization
Best practices
MNIST and CIFAR10 datasets
Image recognition
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)
Using deep neural networks for image classification.
The basic building blocks.
Tips and tricks in image classification .
Object detection basics, classification with localisation.
Different types of Object detection networks – SSD , YOLO.
Learn how to show this object classifying pixels of the image segmentation.
Different concepts of semantic segmentation vs instance segmentation.
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.
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.
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.
Deep convolutional GAN
Generative Adversarial Networks.
We’re going to use a Deep Convolutional GAN to generate images of pokemons. Architecture and implementation.
Summarize & what’s next.
Meet the bigger circle, those you donated and helped to!
© 2019 ALL RIGHTS RESERVED TO GIVE & TECH R.A
ID: 580683225
Address: 1 Shmuel Yavniely Street, Givataim
Email: info@giveandtech.org.il
Tel: +972-532495919
TERMS AND CONDITIONS | PRIVACY POLICY
It’s easy: all we need is your email & your eternal love. But we’ll settle for your email.
