Artificial Intelligence Training

5 Day Training Course: Neural Networks and Deep Learning Enroll in a public course Enroll in a public course Request a private course

5 Day Training Course: Neural Networks and Deep Learning

This five-day hands-on deep learning course is designed for all those seeking a better understanding and knowledge of the major technology trends driving data science.

Attendees will get a clear understanding of the core machine learning concepts, as well as deep learning techniques and engineering solutions for daily usage. You will go through the complete process of building machine learning systems, from data understanding to modelling.

During hands-on labs, accompanying each theoretical unit, you will gain experience in building and applying deep neural networks and machine learning models with such widely used frameworks as Keras, TensorFlow, and scikit-learn.

At the end of the course, the participants will be able to design working scripts that can be used as a basis for creating algorithms to address business-specific challenges.

Day 1

Topic	Description
Core Concepts and Techniques: Theory	An introduction to machine learning tasks and definitions Core principles of building machine learning algorithms A diversity of machine learning algorithms: from linear regression to random forest Core Python packages for machine learning
Core Concepts and Techniques : Labs	Linear and logistic regressions k-nearest neighbors and k-means Decision trees and random forest Handling classification, regression, and clustering tasks *Packages of choice are Pandas/NumPy/scikit-learn

Day 2

Topic	Description
Feature Engineering and Development Methodology: Theory	A wide range of topics related to building machine learning models will be covered on day 2: Feature engineering Dealing with missing data and outliers Dealing with imbalanced classification Advanced validation schemes Handling of model versioning CRISP-DM as a major machine learning development methodology
Feature Engineering and Development Methodology: Labs	Feature engineering: Polynomial and logarithmic features, possible combinations of these features Periodic feature encoding Target encodings Imbalanced classification: Advanced metrics for classification Threshold tuning Over- and undersampling (SMOTE) Missing data handling: Imputation of missing values using k-nearest neighbors or decision trees Advanced validation: Cross-validation for time series *Packages of choice are Pandas/NumPy/scikit-learn

Day 3

Topic	Description
Introduction to Deep Learning: Theory	We’ll look at a surprisingly strong machine learning techniques that have become really popular recently and will cover the following topics: Structure of neural networks, feedforward neural networks A mechanism for learning neural networks Means of neural network learning process control
Introduction to Deep Learning: Labs	Neural networks for supervised learning with Keras *Packages of choice are Pandas/NumPy/scikit-learn/Keras/TensorFlow

Day 4

Topic	Description
Convolutional Neural Networks: Theory	Convolution as the core of the neural network layer for spatial data processing. Topics for the day: Image features and representation learning A convolution layer and a deep convolutional network Supporting layers for convolutional neural networks State-of-the-art architectures for image processing Transfer learning and fine tuning
Convolutional Neural Networks: Labs	We will: Build a convolutional neural network from scratch to learn image classification Fine-tune existing networks to perform image-related tasks on a different data sets *Packages of choice are Pandas/NumPy/scikit-learn/Keras/TensorFlow

Day 5

Topic	Description
Recurrent Neural Networks: Theory	Neural network architecture for sequential data modelling. Topics for the day: Examples of sequential data and related machine learning tasks The vanilla recurrent neural network architecture and its limitations The advanced recurrent neural network layers architecture
Recurrent Neural Networks: Labs	We will implement: Character- and word-level natural language model Image captioning

Anyone who has a basic understanding of Python.

This course is a good fit if you are looking for:

major technology trends driving data science
building, training, and applying deep neural networks
key types of neural network architectures

During the course the students will:

gain a basic understanding of machine and deep learning concepts
learn how to use neural networks to tackle various business problems
go through the complete process of building machine learning solutions

Altoros recommends that all students have:

Basic Python programming skills, a capability to work effectively with data structures
Experience with the Jupyter Notebook applications
Basic experience with Git
Basic knowledge of Statistics
Basic command line operations

A workstation with the following capabilities:

A web browser (Chrome/Firefox)
Internet connection
A firewall allowing outgoing connections on TCP ports 80 and 443
For reasonable execution time, we recommend you to use GPUs.

The following developer utilities should be installed:

Anaconda
Jupyter Notebook

All code will be written in Python with the use of the following libraries:

Pandas/NumPy are the libraries for matrix calculations and data frame operations. We strongly recommend to browse through the available tutorials for these packages, for instance, the official one: https://pandas.pydata.org/pandas-docs/stable/10min.html
scikit-learn
Matplotlib

All these libraries will be installed using Anaconda.

If software requirements cannot be satisfied due to the security policy of your employer, please inform us about the situation to find an appropriate solution for this issue.

At the end of the course, the participants will be able to design working scripts that can be used as a basis for creating algorithms to address business-specific challenges.

PRIVATE TRAINING FOR YOUR TEAM

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Public Classes Schedule and Tickets

London, March 25-29, 2019
Do not see a location that works for you?

If you would like to request a training in your city and/or in your company, please email us at training@altoros.com

Our Trainers

Vladimir Starostenkov Machine Learning Solutions Architect

Kanstantsin Buzanouski Data Scientist, Trainer

Need Private Training or Have Questions?

Natalia Usenia Training Coordinator

training@altoros.com

+1(650)265-2266

Headquarters

830 Stewart Dr., Suite 119 Sunnyvale, CA 94085

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