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Data Analysis

Deep Learning with Python

מספר הקורס 40835

32 סה"כ שעות אקדמאיות
4 מפגשים
* מספר המפגשים והשעות למפגש עשויים להשתנות בין קורס לקורס
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המועדים הקרובים

ניתן לפתוח קורס בהתאמה אישית לארגונים במועד שיתואם עימנו

קורס לקבוצות

הקורס נפתח במתכונת של קבוצה בלבד, בהתאמה אישית לארגונים.
לפרטים נוספים: Muzman@johnbryce.co.il

ספרו לי עוד

Overview

Many challenging problems in diverse areas such as computer vision, speech recognition, and machine language translation have recently made great progress by using an emerging technology called deep learning.

At its core, deep learning is inspired by a simplified model of how the human brain works by building effective hierarchical representations of complex data.

This course will explore applications and theory relevant to problem-solving using deep learning. Using some of the popular python package for machine and deep learning, participants will gain a practical experience all throughout the course.

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On Completion, Delegates will be able to

Build, train and test different kinds of neural networks

Solve prediction tasks including image classification and text processing problems

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Who Should Attend

This course is mainly intended for Data Analysts, Developers and Algorithm developers.

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תכנית הלימודים

Full Syllabus

Introduction

  • Data Science
  • Data Analysis
  • Machine Learning Basics
  • Deep Learning overview
  • Python tools and packages overview

Machine Learning Overview

  • Overview
  • Why Learn
  • Applications
  • Machine Learning Process
  • Learning Types:
    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement learning
  • CRISP-DM
  • Examples

Deep Learning and Neural Networks

  • Deep Learning
  • Neural networks overview
  • The perceptron
  • Network structure and hidden layers
  • Activation functions
  • Training the network
  • Forward and back propagation
  • Loss functions
  • Optimization
  • Weight initialization
  • Regularization
  • Normalization
  • Online and batch training
  • Examples from scratch
  • Tensorflow package
  • Keras package
  • Pytorch
  • Lab

Convolution Neural Networks

  • Overview and applications
  • Convolution operations
  • Layers
  • Activations
  • Polling
  • Building and training a CNN
  • CNN architectures
  • Lab

Recurrent Neural Networks

  • Overview and applications
  • Time series
  • Training RNN
  • LSTM
  • Working with Text
  • Lab

Autoencoders

  • Overview and applications
  • Unsupervised with AE
  • Compressing data
  • Denoising AE
  • Variational AE
  • Lab

Advanced Topics

  • Image classification
  • NLP
  • Word embedding
  • Object detection
  • Other computer vision tasks
  • Sequence modeling
  • Transformers
  • Deploying the solution
Prerequisites
  • Basic understanding of programming concepts
  • Basic python knowledge (ability to write scripts with variables, collections, loops, conditions, writing functions)
  • Some Math background (statistics, linear algebra and calculus)

Schedule Appointment

Fill out the form below, and we will be in touch shortly.

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