Deep Learning & Reinforcement Learning Training
That's an optimal choice for training a team of engineers in 5 days.
Take a well-balanced mixture of theory and practice to start building and applying deep neural networks and machine learning models (with Keras, TensorFlow) from the first training day.
Flexible training details: group size starting from 5 engineers, flexible price depending on the number of attendees, time-table, location, adapting to the corporate specifics.
Why enroll
- Get particular insights of Reinforcement learning (RL) concept, and techniques with a focus on its practical use.
- Learn about Deep learning (DL) concept and techniques sufficient to be used in reinforcement learning.
- By the end you'll have a deep understanding of the technology with practical experience of its usage in real life cases.
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Who should attend
The course was designed for:
- Software Engineers
- Data Scientists
Training program
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This is an introduction to the Deep Learning methods for Machine Learning tasks. During this day, we’ll look at surprisingly strong machine learning techniques that have become really popular recently and will cover the following topics:
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Structure of neural networks, feedforward neural networks
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A mechanism for learning neural networks
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Means of neural network learning process control
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Image processing benefitted drastically from Deep Learning. The main architecture for these tasks is Convolutional Neural Network. Topics for the day will include:
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Image features and representation learning
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A convolution layer and a deep convolutional network
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Supporting layers for convolutional neural networks
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State-of-the-art architectures for image processing
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Transfer learning and fine tuning
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This day is dedicated to the architecture of neural networks that allow to work with sequential data, most notably, texts. During this day we will cover:
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Examples of sequential data and related machine learning tasks
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The vanilla recurrent neural network architecture and its limitations
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The advanced recurrent neural network layers architecture (LSTM, GRU)
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This day is dedicated to establishing a theoretical base of Reinforcement Learning methods. We’ll also look at the most common solutions for Reinforcement Learning:
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Theoretical overview of reinforcement learning task
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Multi-Armed Bandits (acquiring new knowledge and optimizing decisions based on existing knowledge, balance these tasks to maximize their total value)
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Markov Decision Processes (mathematical framework for modeling decision making process in situations where outcomes are partly random and partly under the control of a decision maker)
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Temporal-Difference Methods (reinforcement learning principle that enables online learning from actions directly). Q-learning (method that estimates value of taking an action in different situations)
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Deep Reinforcement Learning combines principles from Reinforcement Learning with Deep Learning. Resulting combination is allowing us to build algorithms that solve complex tasks in different environments.
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Limitations of basic Reinforcement Learning algorithms (and possible tricks to extend the capabilities for classic methods of Reinforcement Learning)
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Deep Q-learning (adaptation of Q-learning algorithm to tackle more complex environments)
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Actor-Critic models (addition to Agent scheme that allows to build more effective algorithms)
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Select location
When
Number of participants
Total price
${{commonPriceString}}
The price rises closer to the training date. Take a minute to buy now!From - To
Number of Delegates
The course could be tailored to suit your needs and objectives. It can also be delivered on your premises if preferred.
Price
From - To
Number of Delegates
The course could be tailored to suit your needs and objectives. It can also be delivered on your premises if preferred.
Price
Training from authorized Partner
Meet us
Kanstantsin Buzanouski is Data Scientist who strives to solve business problems with a defined plan at all stages of the development process.
Kanstantsin’s professional expertise includes:
- considerable hands-on experience in using machine learning and statistical methods in various domains
- solid expertise in solving business problems starting from problem definition up to model fine-tuning and solution deployment
- works with popular frameworks and packages in both R (tidyverse and caret) and Python (pandas/Numpy and scikit-learn)
- profoundly knowledgeable about current trends and approaches to machine learning
Vladimir Starostenkov is a skillful ML/AI Architect with a strong expertise in machine learning and artificial intelligence and solid knowledge about distributed computing and embedded software engineering.
Vladimir’s main competencies encompass:
- 10+ years of experience in software development
- participated in 15 successful project implementations
- specializes in artificial intelligence and machine learning, distributed systems design, NoSQL and Hadoop-based systems benchmarking, permissioned blockchains, data engineering, and development of data-centric apps
- authored a number of research papers, comparing the performance of Apache Cassandra, Redis, MongoDB, and Couchbase
- serves as a trainer and a data evangelist
- an active member of the Open Data Science Community
Our Attendees
Here is what our attendees say about us
Frequently asked questions
Did not find the answer to your question? Drop us a line at training@altoros.com
At least minimum experience in programming is enough to proceed!
You can pay via all major credit cards (including Visa, American Express, MasterCard, Discover, and more) or PayPal. If you would like to get an invoice for your company to pay for this training, please email to training@altoros.com and provide us with the following info: Name of your Company/Division which you would like to be invoiced; Name of the person the invoice should be addressed to; Mailing address; Purchase order # to put on the invoice (if required by your company).
Yes, we also offer a discount for groups of 5 or more people who register together.
Please, email to training@altoros.com. Our team will provide you information about the nearest date to your location.
We'd be glad to provide you with: Post-training support from our trainers to cover the questions that you or your team might have; Advanced training options or classes on a different domain to widen your expertise and technical skillset (Kubernetes, Cloud Foundry, Artificial Intelligence / Machine Learning); Customized consulting services for project delivery.
Yes! Once you have completed our training, you will be issued a certificate that you can print or add to your LinkedIn profile for others to see. Note: the certificate does not represent official academic credit.
Yes, we offer discounts of up to 10% off for early birds who register for the training in advance. Each discount tier has a limited number of seats and all of our discounts are available in limited quantities. Once we sell out of our discounted seats, we move to full price.
The standard group size is 8-10 people. Small class sizes provide students unparalleled access to instructors, who are seasoned industry veterans with experience building and deploying full-scale AI solutions. Note: The class is contingent upon having 7+ attendees. If there aren’t enough students, we will offer you to attend the class in a different location or date. That is also the reason why we ask not to buy flight tickets before the class is confirmed.
We do not participate in the process of application for visa. However, you can use a confirmation that you have registered for our class and bought the ticket for it. This should support your visa application. If your application is declined, we will refund the ticket.
Being a Google Gold Partner and AI Builder Partner Altoros delivers AI projects to the organizations that operate in various markets, like banking and finance, insurance, manufacturing, and others. An experienced AI team already helped 14 companies with machine learning adoption and delivering competitive advantage by utilizing the benefits of the technology. More than 500 people all over the world were trained and adopted the knowledge of our experts.