Deep Learning Training Courses in Egypt

Deep Learning Training Courses

Online or onsite, instructor-led live Deep Learning (DL) training courses demonstrate through hands-on practice the fundamentals and applications of Deep Learning and cover subjects such as deep machine learning, deep structured learning, and hierarchical learning.

Deep Learning training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Deep Learning training can be carried out locally on customer premises in Egypt or in NobleProg corporate training centers in Egypt.

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Deep Learning (DL) Course Outlines in Egypt

Course Name
Duration
Overview
Course Name
Duration
Overview
21 hours
Overview
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.
21 hours
Overview
This instructor-led, live training in Egypt (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.
28 hours
Overview
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
35 hours
Overview
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
28 hours
Overview
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.
7 hours
Overview
In this instructor-led, live training in Egypt, 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.
14 hours
Overview
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
7 hours
Overview
In this instructor-led, live training in Egypt (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.
35 hours
Overview
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
28 hours
Overview
In this instructor-led, live training in Egypt, 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.
28 hours
Overview
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.
21 hours
Overview
This instructor-led, live training in Egypt (online or onsite) is aimed at developers and data scientists who wish to use Tensorflow 2.0 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 2.0.
- Understand the benefits of TensorFlow 2.0 over previous versions.
- Build deep learning models.
- Implement an advanced image classifier.
- Deploy a deep learning model to the cloud, mobile and IoT devices.
14 hours
Overview
This instructor-led, live training in Egypt (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.
14 hours
Overview
This instructor-led, live training in Egypt (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.
21 hours
Overview
This instructor-led, live training in Egypt (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.
14 hours
Overview
This instructor-led, live training in Egypt (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.
21 hours
Overview
This instructor-led, live training in Egypt (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.
21 hours
Overview
This instructor-led, live training in Egypt (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.
21 hours
Overview
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.
14 hours
Overview
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.
21 hours
Overview
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:
21 hours
Overview
Deeplearning4j is an Open-Source Deep-Learning Software for Java and Scala on Hadoop and Spark.

Audience

This course is meant for engineers and developers seeking to utilize DeepLearning4J in their image recognition projects.
21 hours
Overview
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
21 hours
Overview
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
21 hours
Overview
This instructor-led, live training in Egypt (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.
21 hours
Overview
This instructor-led, live training in Egypt (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.
14 hours
Overview
This instructor-led, live training in Egypt (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.
21 hours
Overview
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.
21 hours
Overview
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
14 hours
Overview
This instructor-led, live training in Egypt (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.

Upcoming DL (Deep Learning) Courses in Egypt

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