Deep Learning Training Courses in Pakistan

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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 trainings in Pakistan can be carried out locally on customer premises or in NobleProg corporate training centers.

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Deep Learning Subcategories in Pakistan

TensorFlow Training CoursesTensorFlow
Caffe Training CoursesCaffe
Deeplearning4j Training CoursesDeeplearning4j
Torch Training CoursesTorch
Apache SINGA Training CoursesApache SINGA
Keras Training CoursesKeras
Apache MXNet Training CoursesApache MXNet
OpenVINO Training CoursesOpenVINO
Horovod Training CoursesHorovod
Chainer Training CoursesChainer
TensorFlow Lite Training CoursesTensorFlow Lite
AlphaFold Training CoursesAlphaFold
Stable Diffusion Training CoursesStable Diffusion

Deep Learning (DL) Course Outlines in Pakistan

Course Name
Duration
Overview
Course Name
Duration
Overview

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

21 hours
This instructor-led, live training in Pakistan (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.

DeepSpeed for Deep Learning

21 hours
This instructor-led, live training in Pakistan (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.

AlphaFold

7 hours
This instructor-led, live training in Pakistan (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.

Introduction to Stable Diffusion for Text-to-Image Generation

21 hours
This instructor-led, live training in Pakistan (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.

Advanced Machine Learning with Python

21 hours
In this instructor-led, live training in Pakistan, 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.

Deep Reinforcement Learning with Python

21 hours
This instructor-led, live training in Pakistan (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.

Deep Learning for Telecom (with Python)

28 hours
In this instructor-led, live training in Pakistan, 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: 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

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.

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.

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.

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.

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

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.

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.

Machine Learning and Deep Learning

21 hours
This course covers AI (emphasizing Machine Learning and Deep Learning)

OpenNN: Implementing Neural Networks

14 hours
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.

Introduction Deep Learning and Neural Network for Engineers

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.

Facebook NMT: Setting up a Neural Machine Translation System

7 hours
In this instructor-led, live training, participants will learn how to use Facebook NMT (Fairseq) to carry out translation of sample content. By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution.
Format of the course
  • Part lecture, part discussion, heavy hands-on practice
Note
  • If you wish to use specific source and target language content, please contact us to arrange.

Microsoft Cognitive Toolkit 2.x

21 hours
Microsoft Cognitive Toolkit 2.x (previously CNTK) is an open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain. According to Microsoft, CNTK can be 5-10x faster than TensorFlow on recurrent networks, and 2 to 3 times faster than TensorFlow for image-related tasks. In this instructor-led, live training, participants will learn how to use Microsoft Cognitive Toolkit to create, train and evaluate deep learning algorithms for use in commercial-grade AI applications involving multiple types of data such as data, speech, text, and images. By the end of this training, participants will be able to:
  • Access CNTK as a library from within a Python, C#, or C++ program
  • Use CNTK as a standalone machine learning tool through its own model description language (BrainScript)
  • Use the CNTK model evaluation functionality from a Java program
  • Combine feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs)
  • Scale computation capacity on CPUs, GPUs and multiple machines
  • Access massive datasets using existing programming languages and algorithms
Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
Note
  • If you wish to customize any part of this training, including the programming language of choice, please contact us to arrange.

Amazon DSSTNE: Build a Recommendation System

7 hours
In this instructor-led, live training, participants will learn how to use DSSTNE to build a recommendation application. By the end of this training, participants will be able to:
  • Train a recommendation model with sparse datasets as input
  • Scale training and prediction models over multiple GPUs
  • Spread out computation and storage in a model-parallel fashion
  • Generate Amazon-like personalized product recommendations
  • Deploy a production-ready application that can scale at heavy workloads
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice

T2T: Creating Sequence to Sequence Models for Generalized Learning

7 hours
Tensor2Tensor (T2T) is a modular, extensible library for training AI models in different tasks, using different types of training data, for example: image recognition, translation, parsing, image captioning, and speech recognition. It is maintained by the Google Brain team. In this instructor-led, live training, participants will learn how to prepare a deep-learning model to resolve multiple tasks. By the end of this training, participants will be able to:
  • Install tensor2tensor, select a data set, and train and evaluate an AI model
  • Customize a development environment using the tools and components included in Tensor2Tensor
  • Create and use a single model to concurrently learn a number of tasks from multiple domains
  • Use the model to learn from tasks with a large amount of training data and apply that knowledge to tasks where data is limited
  • Obtain satisfactory processing results using a single GPU
Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice

Advanced Machine Learning with R

21 hours
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.

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

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

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

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

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

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.

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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