Deep Learning With Tensorflow/pytorch Training Course: Build Complex Ai Models

Introduction

Dive into the forefront of artificial intelligence with our Deep Learning with TensorFlow/PyTorch Training Course. This program is designed to equip you with the essential skills to implement deep learning models for tackling complex Big Data applications. In today's data-rich environment, the ability to leverage deep learning is crucial for extracting sophisticated insights and building innovative AI solutions. Our deep learning training course provides hands-on experience and expert guidance, empowering you to develop and deploy cutting-edge models using industry-leading frameworks.

This TensorFlow/PyTorch training delves into the core concepts of neural networks, covering topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. You'll gain expertise in using TensorFlow and PyTorch to build, train, and optimize deep learning models for various applications, including image recognition, natural language processing, and time series analysis. Whether you're a data scientist, machine learning engineer, or AI researcher, this deep learning course will empower you to tackle the most challenging Big Data applications.

Target Audience:

• Data Scientists
• Machine Learning Engineers
• AI Researchers
• Software Developers
• Big Data Engineers
• Deep Learning Enthusiasts
• Anyone needing advanced AI skills

Course Objectives:

• Understand the fundamentals of deep learning and neural networks.
• Master the TensorFlow and PyTorch frameworks for deep learning.
• Develop and train convolutional neural networks (CNNs) for image recognition.
• Implement recurrent neural networks (RNNs) for sequence data and time series analysis.
• Utilize transformers for natural language processing (NLP) tasks.
• Optimize deep learning models for performance and accuracy.
• Deploy deep learning models in production environments.
• Troubleshoot and debug deep learning models and pipelines.
• Implement data security and access control in deep learning workflows.
• Integrate deep learning models with big data platforms.
• Understand how to monitor and maintain deep learning models.
• Explore advanced deep learning techniques and architectures.
• Apply real world use cases for deep learning in Big Data.

Duration

10 Days

Course content

Module 1: Introduction to Deep Learning

• Fundamentals of deep learning and neural networks.
• Overview of TensorFlow and PyTorch frameworks.
• Setting up a development environment.
• Introduction to deep learning concepts and applications.
• Best practices for deep learning.

Module 2: TensorFlow Fundamentals

• Working with TensorFlow tensors and operations.
• Building and training neural networks with Keras.
• Implementing custom layers and models.
• Utilizing TensorFlow Datasets and Data Pipelines.
• Best practices for TensorFlow development.

Module 3: PyTorch Fundamentals

• Working with PyTorch tensors and automatic differentiation.
• Building and training neural networks with PyTorch modules.
• Implementing custom layers and models.
• Utilizing PyTorch DataLoader and Transforms.
• Best practices for PyTorch development.

Module 4: Convolutional Neural Networks (CNNs)

• Architecture and design of CNNs.
• Implementing image classification and object detection.
• Utilizing pre-trained CNN models.
• Optimizing CNNs for performance.
• Best practices for CNN development.

Module 5: Recurrent Neural Networks (RNNs)

• Architecture and design of RNNs.
• Implementing sequence data analysis and time series forecasting.
• Utilizing LSTM and GRU networks.
• Handling variable-length sequences.
• Best practices for RNN development.

Module 6: Transformers for Natural Language Processing (NLP)

• Architecture and design of transformer models.
• Implementing text classification and sentiment analysis.
• Utilizing pre-trained transformer models (e.g., BERT, GPT).
• Fine-tuning transformers for specific NLP tasks.
• Best practices for transformer development.

Module 7: Model Optimization and Tuning

• Optimizing deep learning models for performance.
• Implementing hyperparameter tuning and regularization.
• Utilizing model compression and quantization.
• Handling imbalanced datasets.
• Best practices for model optimization.

Module 8: Model Deployment and Productionization

• Deploying deep learning models in production environments.
• Utilizing containerization and orchestration tools (e.g., Docker, Kubernetes).
• Implementing model serving and API endpoints.
• Monitoring model performance in production.
• Best practices for model deployment.

Module 9: Troubleshooting and Debugging Deep Learning Models

• Debugging deep learning models and pipelines.
• Analyzing model errors and performance issues.
• Utilizing debugging tools and techniques.
• Identifying and resolving model biases.
• Best practices for model troubleshooting.

Module 10: Data Security and Access Control in Deep Learning

• Implementing data security in deep learning workflows.
• Utilizing authentication and authorization.
• Implementing data encryption and masking.
• Auditing and compliance in deep learning.
• Best practices for data security.

Module 11: Integrating Deep Learning with Big Data Platforms

• Integrating deep learning models with Hadoop and Spark.
• Utilizing cloud-based deep learning services (e.g., AWS SageMaker, Azure Machine Learning).
• Implementing real-time deep learning pipelines.
• Best practices for integration.

Module 12: Model Monitoring and Maintenance

• Monitoring model performance and drift.
• Implementing model retraining and updating.
• Utilizing model monitoring tools and techniques.
• Handling model versioning and rollback.
• Best practices for model maintenance.

Module 13: Advanced Deep Learning Techniques

• Generative adversarial networks (GANs).
• Reinforcement learning with deep neural networks.
• Graph neural networks (GNNs).
• Federated learning and privacy-preserving deep learning.
• Advanced techniques for large-scale deep learning.

Module 14: Deep Learning on Cloud Platforms

• Utilizing cloud-based deep learning services.
• Deploying deep learning models on AWS, Azure, and GCP.
• Optimizing cloud resources for deep learning.
• Best practices for cloud-based deep learning.

Module 15: Future Trends in Deep Learning

• Emerging trends in deep learning research and applications.
• Utilizing AI and automation in deep learning workflows.
• Implementing explainable AI (XAI) for deep learning models.
• Best practices for future deep learning.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.