Deep Learning Training in Richmond

This instructor-led, live training in Richmond (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

By the end of this training, participants will be able to:

• Build and train convolutional neural networks (CNNs) using TensorFlow.
• Leverage Google Colab for scalable and efficient cloud-based model development.
• Implement image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Use transfer learning to enhance the performance of CNN models.
• Visualize and interpret the results of image classification models.

This instructor-led, live training in Richmond (online or onsite) is aimed at intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

By the end of this training, participants will be able to:

• Set up and navigate Google Colab for deep learning projects.
• Understand the fundamentals of neural networks.
• Implement deep learning models using TensorFlow.
• Train and evaluate deep learning models.
• Utilize advanced features of TensorFlow for deep learning.

This instructor-led, live training in Richmond (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.

By the end of this training, participants will be able to:

• Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
• Develop and optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on various edge devices.
• Utilize tools and techniques for model conversion and optimization.
• Implement practical Edge AI applications using TensorFlow Lite.

This instructor-led, live training in Richmond (online or onsite) is aimed at advanced-level professionals who wish to specialize in cutting-edge deep learning techniques for NLU.

By the end of this training, participants will be able to:

• Understand the key differences between NLU and NLP models.
• Apply advanced deep learning techniques to NLU tasks.
• Explore deep architectures such as transformers and attention mechanisms.
• Leverage future trends in NLU for building sophisticated AI systems.

This instructor-led, live training in Richmond (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

This instructor-led, live training in Richmond (online or onsite) is aimed at advanced-level professionals who wish to leverage AI techniques to revolutionize drug discovery and development processes.

By the end of this training, participants will be able to:

• Understand the role of AI in drug discovery and development.
• Apply machine learning techniques to predict molecular properties and interactions.
• Use deep learning models for virtual screening and lead optimization.
• Integrate AI-driven approaches into the clinical trial process.

This instructor-led, live training in Richmond (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.

This instructor-led, live training in Richmond (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.

This instructor-led, live training in Richmond (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.

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.

In this instructor-led, live training in Richmond, 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.

This instructor-led, live training in Richmond (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.

In this instructor-led, live training in Richmond, 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.

This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development.

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.

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.

This is a 4 day course introducing AI and it's application. There is an option to have an additional day to undertake an AI project on completion of this course. 

This instructor-led, live training in Richmond (online or onsite) is aimed at intermediate-level data scientists and statisticians who wish to prepare data, build models, and apply machine learning techniques effectively in their professional domains.

By the end of this training, participants will be able to:

• Understand and implement various Machine Learning algorithms.
• Prepare data and models for machine learning applications.
• Conduct post hoc analyses and visualize results effectively.
• Apply machine learning techniques to real-world, sector-specific scenarios.

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

This instructor-led, live training in Richmond (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug.

By the end of this training, participants will be able to:

• Set up the necessary development environment to start developing neural network models.
• Define and implement neural network models using a comprehensible source code.
• Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.

Computer Network ToolKit (CNTK) is Microsoft's Open Source, Multi-machine, Multi-GPU, Highly efficent RNN training machine learning framework for speech, text, and images.

Audience

This course is directed at engineers and architects aiming to utilize CNTK in their projects.

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.

This instructor-led, live training in Richmond (online or onsite) is aimed at researchers and developers who wish to install, set up, customize, and use the DeepMind Lab platform to develop general artificial intelligence and machine learning systems.

By the end of this training, participants will be able to:

• Customize DeepMind Lab to build and run an environment that suits learning and training needs.
• Use DeepMind Lab's 3D simulation environment to train learning agents in a first-person viewpoint.
• Facilitate agent evaluation to develop intelligence in a 3D game-like world.

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.

This instructor-led, live training in Richmond (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.

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

In this instructor-led, live training in Richmond, participants will learn to use Python libraries for NLP as they create an application that processes a set of pictures and generates captions. 

By the end of this training, participants will be able to:

• Design and code DL for NLP using Python libraries.
• Create Python code that reads a substantially huge collection of pictures and generates keywords.
• Create Python Code that generates captions from the detected keywords.

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.

This instructor-led, live training in Richmond (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.

By the end of this training, participants will be able to:

• Install the OpenVINO toolkit.
• Accelerate a computer vision application using an FPGA.
• Execute different CNN layers on the FPGA.
• Scale the application across multiple nodes in a Kubernetes cluster.

This instructor-led, live training in Richmond (online or onsite) is aimed at data scientists who wish to use TensorFlow to analyze potential fraud data.

By the end of this training, participants will be able to:

• Create a fraud detection model in Python and TensorFlow.
• Build linear regressions and linear regression models to predict fraud.
• Develop an end-to-end AI application for analyzing fraud data.

This instructor-led, live training in Richmond (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale it up to run across multiple GPUs in parallel.

By the end of this training, participants will be able to:

• Set up the necessary development environment to start running deep learning trainings.
• Install and configure Horovod to train models with TensorFlow, Keras, PyTorch, and Apache MXNet.
• Scale deep learning training with Horovod to run on multiple GPUs.

Type: Theoretical training with applications decided in advance with the students on Lasagne or Keras according to the educational group

Teaching method: presentation, discussions and case studies

Artificial intelligence, after having disrupted many scientific fields, has begun to revolutionize a large number of economic sectors (industry, medicine, communication, etc.). However, its presentation in the mainstream media is often a fantasy, very far from what the domains of Machine Learning or Deep Learning really are. The purpose of this training is to provide engineers who already have mastery of IT tools (including a basic software programming basis) with an introduction to Deep Learning as well as to its different areas of specialization and therefore to the main network architectures. existing today. If the mathematical basics are covered during the course, a BAC+2 level of mathematics is recommended for greater comfort. It is absolutely possible to ignore the mathematical axis and retain only a “system” vision, but this approach will enormously limit your understanding of the subject.

This instructor-led, live training in Richmond (online or onsite) is aimed at technical persons who wish to apply deep learning model to image recognition applications.

By the end of this training, participants will be able to:

• Install and configure Keras.
• Quickly prototype deep learning models.
• Implement a convolutional network.
• Implement a recurrent network.
• Execute a deep learning model on both a CPU and GPU.

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

This instructor-led, live training in Richmond (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to develop their understanding of machine learning algorithms, deep learning techniques, and AI-driven decision-making. The course provides hands-on experience with machine learning concepts, deep learning models, and practical implementations using R.

By the end of this training, participants will be able to:

• Understand the fundamentals of machine learning and deep learning.
• Apply various machine learning algorithms for regression, classification, clustering, and anomaly detection.
• Use deep learning architectures such as artificial neural networks (ANNs).
• Implement supervised and unsupervised learning models.
• Evaluate model performance and optimize hyperparameters.
• Use R for data analysis, visualization, and machine learning applications.

The training is aimed at people who want to learn the basics of neural networks and their applications.

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

This instructor-led, live training in Richmond (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.

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

In this instructor-led, live training, participants will learn how to set up and use OpenNMT to carry out translation of various sample data sets. The course starts with an overview of neural networks as they apply to machine translation. Participants will carry out live exercises throughout the course to demonstrate their understanding of the concepts learned and get feedback from the instructor.

By the end of this training, participants will have the knowledge and practice needed to implement a live OpenNMT solution.

Source and target language samples will be pre-arranged per the audience's requirements.

Format of the Course

• Part lecture, part discussion, heavy hands-on practice

In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application.

By the end of this training, participants will be able to:

• Understand and implement unsupervised learning techniques
• Apply clustering and classification to make predictions based on real world data.
• Visualize data to quicly gain insights, make decisions and further refine analysis.
• Improve the performance of a machine learning model using hyper-parameter tuning.
• Put a model into production for use in a larger application.
• Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.

This course is an introduction to applying neural networks in real world problems using R-project software.

This instructor-led, live training in Richmond (online or onsite) is aimed at developers and data scientists who wish to use Tensorflow 2.x to build predictors, classifiers, generative models, neural networks and so on.

By the end of this training, participants will be able to:

• Install and configure TensorFlow 2.x.
• Understand the benefits of TensorFlow 2.x over previous versions.
• Build deep learning models.
• Implement an advanced image classifier.
• Deploy a deep learning model to the cloud, mobile and IoT devices.

This instructor-led, live training in Richmond (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.

By the end of this training, participants will be able to:

• Install TensorFlow Lite.
• Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
• Add AI to hardware devices without relying on network connectivity.

In this instructor-led, live training in Richmond (online or onsite), participants will learn how to configure and use TensorFlow Serving to deploy and manage ML models in a production environment.

By the end of this training, participants will be able to:

• Train, export and serve various TensorFlow models.
• Test and deploy algorithms using a single architecture and set of APIs.
• Extend TensorFlow Serving to serve other types of models beyond TensorFlow models.

TensorFlow is a 2nd Generation API of Google's open source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

• understand TensorFlow’s structure and deployment mechanisms
• be able to carry out installation / production environment / architecture tasks and configuration
• be able to assess code quality, perform debugging, monitoring
• be able to implement advanced production like training models, building graphs and logging

This course explores, with specific examples, the application of Tensor Flow to the purposes of image recognition

Audience

This course is intended for engineers seeking to utilize TensorFlow for the purposes of Image Recognition

After completing this course, delegates will be able to:

• understand TensorFlow’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, building graphs and logging

This instructor-led, live training in Richmond (online or onsite) is aimed at data scientists who wish to go from training a single ML model to deploying many ML models to production.

By the end of this training, participants will be able to:

• Install and configure TFX and supporting third-party tools.
• Use TFX to create and manage a complete ML production pipeline.
• Work with TFX components to carry out modeling, training, serving inference, and managing deployments.
• Deploy machine learning features to web applications, mobile applications, IoT devices and more.

In this instructor-led, live training in Richmond, participants will learn how to take advantage of the innovations in TPU processors to maximize the performance of their own AI applications.

By the end of the training, participants will be able to:

• Train various types of neural networks on large amounts of data.
• Use TPUs to speed up the inference process by up to two orders of magnitude.
• Utilize TPUs to process intensive applications such as image search, cloud vision and photos.

TensorFlow™ is an open source software library for numerical computation using data flow graphs.

SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.

Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).

Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.

Audience

This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.

After completing this course, delegates will:

• understand TensorFlow’s structure and deployment mechanisms
• be able to carry out installation / production environment / architecture tasks and configuration
• be able to assess code quality, perform debugging, monitoring
• be able to implement advanced production like training models, embedding terms, building graphs and logging

This course begins with giving you conceptual knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).

Part-1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part-2(20%) of this training introduces Theano - a python library that makes writing deep learning models easy.

Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

• have a good understanding on deep neural networks(DNN), CNN and RNN

• understand TensorFlow’s structure and deployment mechanisms

• be able to carry out installation / production environment / architecture tasks and configuration

• be able to assess code quality, perform debugging, monitoring

• be able to implement advanced production like training models, building graphs and logging