Deep Learning Training in Minnesota

This instructor-led, live training in Minnesota (online or onsite) is aimed at intermediate-level finance professionals, fintech developers, and AI specialists who wish to implement Edge AI solutions in financial services.By the end of this training, participants will be able to:

• Understand the role of Edge AI in financial services.
• Implement fraud detection systems using Edge AI.
• Enhance customer service through AI-driven solutions.
• Apply Edge AI for risk management and decision-making.
• Deploy and manage Edge AI solutions in financial environments.

This instructor-led, live training in Minnesota (online or onsite) is aimed at intermediate-level industrial engineers, manufacturing professionals, and AI developers who wish to implement Edge AI solutions in industrial automation.By the end of this training, participants will be able to:

• Understand the role of Edge AI in industrial automation.
• Implement predictive maintenance solutions using Edge AI.
• Apply AI techniques for quality control in manufacturing processes.
• Optimize industrial processes using Edge AI.
• Deploy and manage Edge AI solutions in industrial environments.

This instructor-led, live training in Minnesota (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 Minnesota (online or onsite) is aimed at intermediate-level AI developers, machine learning engineers, and system architects who wish to optimize AI models for edge deployment.By the end of this training, participants will be able to:

• Understand the challenges and requirements of deploying AI models on edge devices.
• Apply model compression techniques to reduce the size and complexity of AI models.
• Utilize quantization methods to enhance model efficiency on edge hardware.
• Implement pruning and other optimization techniques to improve model performance.
• Deploy optimized AI models on various edge devices.

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.

This instructor-led, live training in Minnesota (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 Minnesota (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 Minnesota (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 Minnesota (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.

In this instructor-led, live training in Minnesota, 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 Minnesota (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 Minnesota, 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.

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

This instructor-led, live training in Minnesota (online or onsite) is aimed at developers who wish to use TensorFlow Lite to deploy deep learning models on embedded devices. By the end of this training, participants will be able to:

• Install and configure Tensorflow Lite on an embedded device.
• Understand the concepts and components underlying TensorFlow Lite.
• Convert existing models to TensorFlow Lite format for execution on embedded devices.
• Work within the limitations of small devices and TensorFlow Lite, while learning how to expand the scope of operations that can be run.
• Deploy a deep learning model on an embedded device running Linux.

This instructor-led, live training in Minnesota (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop mobile applications with deep learning capabilities. By the end of this training, participants will be able to:

• Install and configure TensorFlow Lite.
• Understand the principles behind TensorFlow, machine learning and deep learning.
• Load TensorFlow Models onto an Android device.
• Enable deep learning and machine learning functionality such as computer vision and natural language recognition in a mobile application.

This instructor-led, live training in (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop iOS mobile applications with deep learning capabilities. By the end of this training, participants will be able to:

• Install and configure TensorFlow Lite.
• Understand the principles behind TensorFlow and machine learning on mobile devices.
• Load TensorFlow Models onto an iOS device.
• Run an iOS application capable of detecting and classifying an object captured through the device's camera.

This instructor-led, live training in Minnesota (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.

This instructor-led, live training in Minnesota (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.

This instructor-led, live training in Minnesota (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.

This instructor-led, live training in Minnesota (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 (online or onsite) is aimed at data scientists who wish to use Apache MXNet's to build and deploy a deep learning model for image recognition. By the end of this training, participants will be able to:

• Install and configure Apache MXNet and its components.
• Understand MXNet's architecture and data structures.
• Use Apache MXNet's low-level and high-level APIs to efficiently build neural networks.
• Build a convolutional neural network for image classification.

This instructor-led, live training in Minnesota (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.

This instructor-led, live training in Minnesota (online or onsite) is aimed at software engineers who wish to develop advanced deep learning neural-networks and model using Keras and Python. By the end of this training, participants will be able to:

• Apply deep learning with supervised or unsupervised learning methods.
• Develop, train, and implement concurrent neural networks and recurrent neural networks.
• Use Keras and Python to build deep learning models to solve problems involving images, text, sound, and more.

This instructor-led, live training in Minnesota (online or onsite) is aimed at developers who wish to build a self-driving car using deep learning techniques. By the end of this training, participants will be able to:

• Use Keras to build and train a convolutional neural network.
• Use computer vision techniques to identify lanes in an autonomos driving project.
• Train a deep learning model to differentiate traffic signs.
• Simulate a fully autonomous car.

Torch is an open source machine learning library and a scientific computing framework based on the Lua programming language. It provides a development environment for numerics, machine learning, and computer vision, with a particular emphasis on deep learning and convolutional nets. It is one of the fastest and most flexible frameworks for Machine and Deep Learning and is used by companies such as Facebook, Google, Twitter, NVIDIA, AMD, Intel, and many others. In this instructor-led, live training, we cover the principles of Torch, its unique features, and how it can be applied in real-world applications. We step through numerous hands-on exercises all throughout, demonstrating and practicing the concepts learned. By the end of the course, participants will have a thorough understanding of Torch's underlying features and capabilities as well as its role and contribution within the AI space compared to other frameworks and libraries. Participants will have also received the necessary practice to implement Torch in their own projects.

Format of the Course

• Overview of Machine and Deep Learning
• In-class coding and integration exercises
• Test questions sprinkled along the way to check understanding

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

SINGA is a general distributed deep learning platform for training big deep learning models over large datasets. It is designed with an intuitive programming model based on the layer abstraction. A variety of popular deep learning models are supported, namely feed-forward models including convolutional neural networks (CNN), energy models like restricted Boltzmann machine (RBM), and recurrent neural networks (RNN). Many built-in layers are provided for users. SINGA architecture is sufficiently flexible to run synchronous, asynchronous and hybrid training frameworks. SINGA also supports different neural net partitioning schemes to parallelize the training of large models, namely partitioning on batch dimension, feature dimension or hybrid partitioning. Audience This course is directed at researchers, engineers and developers seeking to utilize Apache SINGA as a deep learning framework. After completing this course, delegates will:

• understand SINGA’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

 

Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs.   Audience This course is directed at engineers and developers seeking to utilize Deeplearning4j in their projects.   After this course delegates will be able to:

Deeplearning4j is an open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs. Word2Vec is a method of computing vector representations of words introduced by a team of researchers at Google led by Tomas Mikolov. Audience This course is directed at researchers, engineers and developers seeking to utilize Deeplearning4J to construct Word2Vec models.

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. 

In this instructor-led, live training in Minnesota, 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 Minnesota (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 (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an OpenShift on-premise or hybrid cloud.

• By the end of this training, participants will be able to:
• Install and configure Kubernetes and Kubeflow on an OpenShift cluster.
• Use OpenShift to simplify the work of initializing a Kubernetes cluster.
• Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
• Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
• Call public cloud services (e.g., AWS services) from within OpenShift to extend an ML application.

This course will give you knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications). This training is more focus on fundamentals, but will help you to choose the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.

This instructor-led, live training in Minnesota (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 Minnesota (online or onsite) is aimed at data scientists who wish to use TensorFlow.js to identify patterns and generate predictions through machine learning models. By the end of this training, participants will be able to:

• Build and train machine learning models with TensorFlow.js.
• Run existing machine learning models in the browser or under Node.js.
• Retrain pre-existing machine learning using custom data.

In this instructor-led, live training in Minnesota (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 Minnesota (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 Minnesota, 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

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 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.

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.

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.

This instructor-led, live training in Minnesota (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. 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

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

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

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

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.

This instructor-led, live training in Minnesota (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.

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.

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 course covers AI (emphasizing Machine Learning and Deep Learning)

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 Minnesota (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, we go over the principles of neural networks and use OpenNN to implement a sample application.

Format of the course

• Lecture and discussion coupled with hands-on exercises.

PaddlePaddle (PArallel Distributed Deep LEarning) is a scalable deep learning platform developed by Baidu. In this instructor-led, live training, participants will learn how to use PaddlePaddle to enable deep learning in their product and service applications. By the end of this training, participants will be able to:

• Set up and configure PaddlePaddle
• Set up a Convolutional Neural Network (CNN) for image recognition and object detection
• Set up a Recurrent Neural Network (RNN) for sentiment analysis
• Set up deep learning on recommendation systems to help users find answers
• Predict click-through rates (CTR), classify large-scale image sets, perform optical character recognition(OCR), rank searches, detect computer viruses, and implement a recommendation system.

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 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 instructor-led, live training in Minnesota (online or onsite) is aimed at developers who wish to build hardware-accelerated object detection and tracking models to analyze streaming video data. By the end of this training, participants will be able to:

• Install and configure the necessary development environment, software and libraries to begin developing.
• Build, train, and deploy deep learning models to analyze live video feeds.
• Identify, track, segment and predict different objects within video frames.
• Optimize object detection and tracking models.
• Deploy an intelligent video analytics (IVA) application.