Udemy – Applied Deep Learning & Neural Network: Practical AI Mastery 2024-1

Udemy – Applied Deep Learning & Neural Network: Practical AI Mastery 2024-1 Downloadly IRSpace

Apr 18, 2025 - 19:58 Updated: Aug 17, 2025 - 19:16

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Udemy – Applied Deep Learning & Neural Network: Practical AI Mastery 2024-1

Applied Deep Learning & Neural Network: Practical AI Mastery. This course provides a comprehensive, hands-on guide to deep learning and neural networks, equipping you with the essential skills to enter the field of artificial intelligence and machine learning. First, you’ll learn the basics of machine learning and the basic concepts of deep learning, then you’ll explore the structure of neural networks and the global approximation theorem. In the practical sections, you’ll learn how to code with tools like Jupyter Notebooks, Google Colab, and the PyTorch library, and work with concepts like tensors and gradients. The course includes practical examples, such as working with the MNIST and CIFAR-10 datasets, where neural networks are implemented and evaluated. You’ll also experience transfer learning and image classification using convolutional networks (CNNs). In the Natural Language Processing section, you will learn about text classification methods, text generation with transformers, and machine translation with encoder-decoder architectures. Ultimately, this course prepares you to solve real-world problems, from tabular data prediction to recommender systems, and provides a deep understanding of deep learning and its applications by combining theory and practice.

What you will learn:

Understand the fundamental concepts: Understand the fundamental principles of machine learning, explore common methods, and discover the core ideas behind DL.
Practical coding experience: Gain practical coding skills using platforms like Jupyter Notebooks, Google Colab, and PyTorch and apply theoretical knowledge.
Proficiency in Neural Networks: Gain a deep understanding of neural networks, their structures, and applications, and build a foundation for advanced topics.
Transfer Learning Applications: Explore transfer learning, apply pre-trained models to new tasks, and work with datasets like CIFAR-10.
Text-based applications: Extend your skills to text classification and generation and harness the power of convolutional neural networks and transformer architecture.
Text Translation and Beyond: Master text translation using encoder-decoder architectures and explore diverse applications, including tabular data prediction.
Implement real-world projects: Apply the knowledge you gain to real-world projects, such as image classification and text translation, and hone your skills.
Practical advice: Gain insights into best practices, recommendations, and effective learning strategies to ensure constructive understanding.
At the end of this course, students will have the practical skills and theoretical knowledge necessary to navigate the dynamic landscape of deep learning.

Who is this course suitable for?

Data Science enthusiasts: People who are looking to enter the field of data science and machine learning and whose goal is to build practical skills and gain practical experience.
AI enthusiasts: Those who are interested in artificial intelligence and are looking to deepen their understanding of deep learning, neural networks, and practical applications in real-world projects.
Programmers and Developers: Professionals with a programming background who are interested in expanding their expertise to include deep learning and neural network applications.
Technology professionals: People in technology-related fields who want to stay up to date with the latest AI developments and increase their skills in applied deep learning.
Students and researchers: Students studying computer science, engineering, or related fields, as well as researchers interested in practical applications of deep learning techniques.
Professionals looking to make a career change: Individuals who plan to move into roles focused on machine learning, AI, or data science and want to gain practical skills for successful career transitions.