What you'll get
- 11+ Hours
- 1 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Understand the fundamental principles of AI-powered deep learning.
- Learn essential concepts such as neural networks, perception, and the Universal Approximation Theorem.
- Set up environments and write code using Jupyter Notebook, Google Colab, and PyTorch.
- Perform data preprocessing, including handling tensors, gradients, and datasets like MNIST.
- Explore advanced deep learning topics, including image classification, text classification, and text generation.
- Apply transfer learning and convolutional neural networks (CNNs) for image and text tasks.
- Build and train models for text classification and generation, including transformer architectures and attention mechanisms.
- Implement text translation and collaborative filtering for recommendation systems.
- Get practical experience by working on real-world projects and applications.
- Understand the theory, mathematics, and architecture behind feedforward and convolutional neural networks.
- Build models from scratch in PyTorch, including gradient descent and network fine-tuning.
- Learn Python from scratch, with no prior coding experience necessary.
- Explore autoencoders, regularization, and techniques to improve model performance.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Comprehensive Deep Learning Training | 11h 17m | ✔ | View Curriculum |
Description
This course provides a clear and thorough introduction to deep learning, a key area of machine learning that brings us closer to achieving true artificial intelligence. Learners begin by understanding the core principles of machine learning, how algorithms learn from data, and how deep learning expands these capabilities through advanced neural network architectures. The training focuses on essential deep learning concepts, including neural networks, perception, the Universal Approximation Theorem, gradient-based learning, and the mathematical foundations of modern AI systems.
Participants will learn to set up their environment with tools such as Jupyter Notebook, Google Colab, and PyTorch, and to work with core deep learning elements, including tensors, gradients, and datasets such as MNIST. The course covers major application areas, including image classification with convolutional neural networks (CNNs), text classification, text generation with transformer models, and text translation using encoder–decoder architectures with attention mechanisms. Learners also explore predictive modeling on tabular data and collaborative filtering techniques for building recommendation systems.
Throughout the program, the focus remains on applying deep learning methods to practical, real-world scenarios. The course combines theory and hands-on projects to teach participants how to build, train, evaluate, and optimize deep learning models for applications like computer vision, NLP, and recommendations, providing both foundational knowledge and practical expertise.
Requirements
- Basic understanding of machine learning concepts.
- Familiarity with Python programming.
Target Audience
- Aspiring data scientists seeking to build foundational and practical skills.
- AI, machine learning, and deep learning engineers aiming to enhance their expertise.
