Bristol

Citypoint, Temple Gate, Bristol, united kingdom, BS1 6PL

The training rooms are located under a 2 minute walk from Bristol Temple Meads Railway station. From the main exit at Temp...

Bath

University of Bath Innovation Centre, Carpenter House, Bath, united kingdom, BA1 1UD,

Why not try somewhere different as a location for your training. Come to Bath which is located at the southern tip of the ...

Subcategories (13)

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Stable Diffusion
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TensorFlow
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Caffe
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Deeplearning4j
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Torch
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Apache SINGA
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Keras
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Apache MXNet
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OpenVINO
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Horovod
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Chainer
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TensorFlow Lite
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AlphaFold

Explore Our Courses

Advanced Stable Diffusion: Deep Learning for Text-to-Image Generation

21 Hours
This instructor-led, live training in Bristol (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation. By the end of this training, participants will be able to:
  • Understand advanced deep learning architectures and techniques for text-to-image generation.
  • Implement complex models and optimizations for high-quality image synthesis.
  • Optimize performance and scalability for large datasets and complex models.
  • Tune hyperparameters for better model performance and generalization.
  • Integrate Stable Diffusion with other deep learning frameworks and tools
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DeepSpeed for Deep Learning

21 Hours
This instructor-led, live training in Bristol (online or onsite) is aimed at beginner to intermediate-level data scientists and machine learning engineers who wish to improve the performance of their deep learning models. By the end of this training, participants will be able to:
  • Understand the principles of distributed deep learning.
  • Install and configure DeepSpeed.
  • Scale deep learning models on distributed hardware using DeepSpeed.
  • Implement and experiment with DeepSpeed features for optimization and memory efficiency.
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Introduction to Large Language Models (LLMs)

14 Hours
This instructor-led, live training in Bristol (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use Large Language Models for various natural language tasks. By the end of this training, participants will be able to:
  • Set up a development environment that includes a popular LLM.
  • Create a basic LLM and fine-tune it on a custom dataset.
  • Use LLMs for different natural language tasks such as text summarization, question answering, text generation, and more.
  • Debug and evaluate LLMs using tools such as TensorBoard, PyTorch Lightning, and Hugging Face Datasets.
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AlphaFold

7 Hours
This instructor-led, live training in Bristol (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies. By the end of this training, participants will be able to:
  • Understand the basic principles of AlphaFold.
  • Learn how AlphaFold works.
  • Learn how to interpret AlphaFold predictions and results.
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Introduction to Stable Diffusion for Text-to-Image Generation

21 Hours
This instructor-led, live training in (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases. By the end of this training, participants will be able to:
  • Understand the principles of Stable Diffusion and how it works for image generation.
  • Build and train Stable Diffusion models for image generation tasks.
  • Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
  • Optimize the performance and stability of Stable Diffusion models.
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Advanced Machine Learning with Python

21 Hours
In this instructor-led, live training in Bristol, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build a series of demo applications involving image, music, text, and financial data. By the end of this training, participants will be able to:
  • Implement machine learning algorithms and techniques for solving complex problems.
  • Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
  • Push Python algorithms to their maximum potential.
  • Use libraries and packages such as NumPy and Theano.
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Deep Reinforcement Learning with Python

21 Hours
This instructor-led, live training in Bristol (online or onsite) is aimed at developers and data scientists who wish to learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent. By the end of this training, participants will be able to:
  • Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning.
  • Apply advanced Reinforcement Learning algorithms to solve real-world problems.
  • Build a Deep Learning Agent.
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Deep Learning for Telecom (with Python)

28 Hours
In this instructor-led, live training in Bristol, participants will learn how to implement deep learning models for telecom using Python as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to:
  • Understand the fundamental concepts of deep learning.
  • Learn the applications and uses of deep learning in telecom.
  • Use Python, Keras, and TensorFlow to create deep learning models for telecom.
  • Build their own deep learning customer churn prediction model using Python.
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Embedding Projector: Visualizing Your Training Data

14 Hours
Embedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow. This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project. By the end of this training, participants will be able to:
  • Explore how data is being interpreted by machine learning models
  • Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it
  • Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals.
  • Explore the properties of a specific embedding to understand the behavior of a model
  • Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers
Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Artificial Intelligence (AI) in Automotive

14 Hours
This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making.
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Artificial Neural Networks, Machine Learning, Deep Thinking

21 Hours
Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.
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Artificial Neural Networks, Machine Learning and Deep Thinking

21 Hours
Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.
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Deep Learning for Vision with Caffe

21 Hours
Caffe is a deep learning framework made with expression, speed, and modularity in mind. This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example Audience This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework. After completing this course, delegates will be able to:
  • understand Caffe’s structure and deployment mechanisms
  • carry out installation / production environment / architecture tasks and configuration
  • assess code quality, perform debugging, monitoring
  • implement advanced production like training models, implementing layers and logging
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Introduction to Deep Learning

21 Hours
This course is general overview for Deep Learning without going too deep into any specific methods. It is suitable for people who want to start using Deep learning to enhance their accuracy of prediction.
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Advanced Deep Learning

28 Hours
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks.
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Deep Learning AI Techniques for Executives, Developers and Managers

21 Hours
Introduction: Deep learning is becoming a principal component of future product design that wants to incorporate artificial intelligence at the heart of their models. Within the next 5 to 10 years, deep learning development tools, libraries, and languages will become standard components of every software development toolkit. So far Google, Sales Force, Facebook, Amazon have been successfully using deep learning AI to boost their business. Applications ranged from automatic machine translation, image analytics, video analytics, motion analytics, generating targeted advertisement and many more. This coursework is aimed for those organizations who want to incorporate Deep Learning as very important part of their product or service strategy. Below is the outline of the deep learning course which we can customize for different levels of employees/stakeholders in an organization. Target Audience: ( Depending on target audience, course materials will be customized) Executives A general overview of AI and how it fits into corporate strategy, with breakout sessions on strategic planning, technology roadmaps, and resource allocation to ensure maximum value. Project Managers How to plan out an AI project, including data gathering and evaluation, data cleanup and verification, development of a proof-of-concept model, integration into business processes, and delivery across the organization. Developers In-depth technical trainings, with focus on neural networks and deep learning, image and video analytics (CNNs), sound and text analytics (NLP), and bringing AI into existing applications. Salespersons A general overview of AI and how it can satisfy customer needs, value propositions for various products and services, and how to allay fears and promote the benefits of AI.
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Deep Learning for Business

14 Hours
This instructor-led, live training in Bristol (online or onsite) is aimed at business analysts, data scientists, and developers who wish to build and implement deep learning models to accelerate revenue growth and solve problems in the business world. By the end of this training, participants will be able to:
  • Understand the core concepts of machine learning and deep learning.
  • Get insights on the future of business and industry with ML and DL.
  • Define business strategies and solutions with deep learning.
  • Learn how to apply data science and deep learning in solving business problems.
  • Build deep learning models using Python, Pandas, TensorFlow, CNTK, Torch, Keras, etc.
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Deep Learning for Finance (with R)

28 Hours
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to:
  • Understand the fundamental concepts of deep learning
  • Learn the applications and uses of deep learning in finance
  • Use R to create deep learning models for finance
  • Build their own deep learning stock price prediction model using R
Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Deep Learning for Banking (with Python)

28 Hours
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to:
  • Understand the fundamental concepts of deep learning
  • Learn the applications and uses of deep learning in banking
  • Use Python, Keras, and TensorFlow to create deep learning models for banking
  • Build their own deep learning credit risk model using Python
Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
Read more...

Deep Learning for Banking (with R)

28 Hours
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to:
  • Understand the fundamental concepts of deep learning
  • Learn the applications and uses of deep learning in banking
  • Use R to create deep learning models for banking
  • Build their own deep learning credit risk model using R
Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
Read more...

Deep Learning for Finance (with Python)

28 Hours
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to:
  • Understand the fundamental concepts of deep learning
  • Learn the applications and uses of deep learning in finance
  • Use Python, Keras, and TensorFlow to create deep learning models for finance
  • Build their own deep learning stock price prediction model using Python
Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
Read more...

Deep Learning for Medicine

14 Hours
Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep Learning is a subfield of Machine Learning which attempts to mimic the workings of the human brain in making decisions. It is trained with data in order to automatically provide solutions to problems. Deep Learning provides vast opportunities for the medical industry which is sitting on a data goldmine. In this instructor-led, live training, participants will take part in a series of discussions, exercises and case-study analysis to understand the fundamentals of Deep Learning. The most important Deep Learning tools and techniques will be evaluated and exercises will be carried out to prepare participants for carrying out their own evaluation and implementation of Deep Learning solutions within their organizations. By the end of this training, participants will be able to:
  • Understand the fundamentals of Deep Learning
  • Learn Deep Learning techniques and their applications in the industry
  • Examine issues in medicine which can be solved by Deep Learning technologies
  • Explore Deep Learning case studies in medicine
  • Formulate a strategy for adopting the latest technologies in Deep Learning for solving problems in medicine
Audience
  • Managers
  • Medical professionals in leadership roles
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
Note
  • To request a customized training for this course, please contact us to arrange.
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Deep Learning for Healthcare

14 Hours
This instructor-led, live training in Bristol (online or onsite) is aimed at developers and data scientists who wish to apply convolutional neural networks (CNNs) to the analysis of MRI scans. By the end of this training, participants will be able to:
  • Install and configure the necessary development environment, software and libraries to begin developing.
  • Analyze MRI images using deep learning techniques such as CNNs.
  • Detect potential health conditions such as heart disease through MRI scan analysis.
  • Apply techniques such as image segmentation and CNN training to identify potential disease.
  • Identify the genomics of a disease using radiomics.
  • Build and deploy a deep learning application aimed at healthcare image analysis.
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Deep Learning for Vision

21 Hours
Audience This course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source) for analyzing computer images This course provide working examples.
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Introduction Deep Learning & Réseaux de neurones pour l’ingénieur

21 Hours
Artificial intelligence has revolutionized a large number of economic sectors (industry, medicine, communication, etc.) after having upset many scientific fields. Nevertheless, his presentation in the major media is often a fantasy, far removed from what really are the fields of Machine Learning or Deep Learning. The aim of this course is to provide engineers who already have a master's degree in computer tools (including a software programming base) an introduction to Deep Learning as well as to its various fields of specialization and therefore to the main existing network architectures today. If the mathematical bases are recalled during the course, a level of mathematics of type BAC + 2 is recommended for more comfort. It is absolutely possible to ignore the mathematical axis in order to maintain only a "system" vision, but this approach will greatly limit your understanding of the subject.
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Matlab for Deep Learning

14 Hours
In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition. By the end of this training, participants will be able to:
  • Build a deep learning model
  • Automate data labeling
  • Work with models from Caffe and TensorFlow-Keras
  • Train data using multiple GPUs, the cloud, or clusters
Audience
  • Developers
  • Engineers
  • Domain experts
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
Read more...

Machine Learning and Deep Learning

21 Hours
This course covers AI (emphasizing Machine Learning and Deep Learning)
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Neural computing – Data science

14 Hours
This classroom based training session will contain presentations and computer based examples and case study exercises to undertake with relevant neural and deep network libraries
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Python and Deep Learning with OpenCV 4

14 Hours
This instructor-led, live training in Bristol (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning. By the end of this training, participants will be able to:
  • View, load, and classify images and videos using OpenCV 4.
  • Implement deep learning in OpenCV 4 with TensorFlow and Keras.
  • Run deep learning models and generate impactful reports from images and videos.
Read more...

OpenFace: Creating Facial Recognition Systems

14 Hours
OpenFace is Python and Torch based open-source, real-time facial recognition software based on Google's FaceNet research. In this instructor-led, live training, participants will learn how to use OpenFace's components to create and deploy a sample facial recognition application. By the end of this training, participants will be able to:
  • Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation
  • Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc.
Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
Read more...

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Testimonials (21)

Working from first principles in a focused way, and moving to applying case studies within the same day

Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking

It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.

Jonathan Blease
Course - Artificial Neural Networks, Machine Learning, Deep Thinking

Very flexible.

Frank Ueltzhöffer
Course - Artificial Neural Networks, Machine Learning and Deep Thinking

The structure from first principles, to case studies, to application.

Margaret Webb - Department of Jobs, Regions, and Precincts
Course - Introduction to Deep Learning

The deep knowledge of the trainer about the topic.

Sebastian Görg
Course - Introduction to Deep Learning

I think that if training would be done in polish it would allow the trainer to share his knowledge more efficient.

Radek
Course - Introduction to Deep Learning

Exercises after each topic were really helpful, despite there were too complicated at the end. In general, the presented material was very interesting and involving! Exercises with image recognition were great.

Dolby Poland Sp. z o.o.
Course - Introduction to Deep Learning

I was benefit from the passion to teach and focusing on making thing sensible.

Zaher Sharifi - GOSI
Course - Advanced Deep Learning

examples based on our data

Witold - P4 Sp. z o.o.
Course - Deep Learning for Telecom (with Python)

code examples:-)

Marcin - P4 Sp. z o.o.
Course - Deep Learning for Telecom (with Python)

I liked that the instructor had many pre-written scripts to show off many different aspects of ML and AI. I really enjoyed being able to see live demos of so many ways ML and AI is being used. Much of what we covered was cutting edge technology that is still in its early stages of development.

Matthew Pepper - Motorola Solutions
Course - Deep Learning for Telecom (with Python)

The colab notebooks we get to keep

Palmer Greer - Motorola Solutions
Course - Deep Learning for Telecom (with Python)

The clarity with which it was presented

John McLemore - Motorola Solutions
Course - Deep Learning for Telecom (with Python)

We had an overview about Machine Learning, Neural Networks, AI with practical examples.

Catalin - DB Global Technology SRL
Course - Machine Learning and Deep Learning

Last day with the AI

Ovidiu - DB Global Technology SRL
Course - Machine Learning and Deep Learning

The examples that were picked, shared with us and explained

Cristina - DB Global Technology SRL
Course - Machine Learning and Deep Learning

I really enjoyed the coverage and depth of topics.

Anirban Basu
Course - Machine Learning and Deep Learning

The training provided the right foundation that allows us to further to expand on, by showing how theory and practice go hand in hand. It actually got me more interested in the subject than I was before.

Jean-Paul van Tillo
Course - Machine Learning and Deep Learning

In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.

Sacha Nandlall
Course - Python for Advanced Machine Learning

Very updated approach or CPI (tensor flow, era, learn) to do machine learning.

Paul Lee
Course - TensorFlow for Image Recognition

Tomasz really know the information well and the course was well paced.

Raju Krishnamurthy - Google
Course - TensorFlow Extended (TFX)

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