Udemy - Deep Learning by RAHUL RAI

Level: All | Genre: eLearning | Language: English | Duration: 83 Lectures ( 11h 29m ) | Size: 14.6 GB
Deep Learning, Neural Networks, Backpropagation, Activation Functions, Gradient Descent, CNNs, RNNs, Transfer Learning.
What you'll learn
Understand the fundamental concepts of deep learning, including neural networks and their architectures.
Learn key techniques such as backpropagation, activation functions, and gradient descent.
Gain hands-on experience with deep learning frameworks (e.g., TensorFlow or PyTorch).
Explore methods for processing and analyzing large-scale datasets.
Develop skills to train, evaluate, and optimize deep neural networks.
Apply deep learning to extract actionable insights from data.
Requirements
To succeed, you should understand linear algebra, calculus, probability, and basic Python, along with machine learning concepts. Experience with neural networks is helpful but not required.
Description
The Deep Learning course offers a comprehensive and self-contained introduction. It is designed to provide a balanced approach that combines theory, numerical methods, and hands-on programming. The goal is to equip learners with the essential skills and knowledge required to understand and apply deep learning techniques to real-world problems. The course will cover a wide range of deep learning topics, addressing foundational concepts and advanced techniques crucial for processing large datasets and generating actionable insights through deep neural networks. The primary focus is on using deep learning to process big data, specifically emphasising how these models can be trained, tested, and implemented to tackle complex problems.Through theoretical lessons, learners will explore the principles behind neural networks, backpropagation, optimisation algorithms, and various architectures, such as convolutional and recurrent networks. Additionally, the course integrates programming assignments that allow students to apply the concepts learned in practice. These assignments will utilise popular deep learning frameworks, enabling learners to build and train neural networks on real-world datasets.The course structure emphasises a basic concept-learning approach to ensure a solid understanding of deep learning fundamentals before advancing to more complex topics. By the end of the course, students will have a robust knowledge of deep learning, enabling them to process large-scale data, build powerful models, and extract valuable insights from diverse datasets.
Who this course is for
This course is for students, researchers, and professionals seeking a solid foundation in deep learning, with a balance of theory, programming, and practical insights for processing big data. It suits anyone with a basic background in math and programming eager to explore neural networks hands-on.
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