Description
“Hands-on Deep Learning with PyTorch: From Math to Modern AI” is a course on the fundamentals of deep learning and practical artificial intelligence engineering skills, published by the online academy Udemy. This comprehensive course combines essential mathematical concepts with hands-on programming to help you understand how modern neural networks function and how to build them using PyTorch. You will learn the mathematics behind machine learning—including linear algebra, calculus, probability, and optimization—while discovering how these concepts power neural networks and intelligent systems. You will gain practical experience with tensors, automatic differentiation, model architecture, training loops, loss functions, optimizers, and performance evaluation. The course covers key deep learning techniques and modern AI applications, such as computer vision, natural language processing, and advanced neural network models.
Through practical implementations and real-world examples, you will develop the ability to design, train, debug, and improve deep learning models, all while gaining a deeper understanding of the technologies driving today’s AI breakthroughs. Whether you are a Python developer, an aspiring machine learning engineer, a data scientist, or simply an AI enthusiast, this course will help you bridge the gap between mathematical theory and the practical development of deep learning systems using PyTorch. Throughout the course, you will work with sequential data, RNNs, and LSTMs; implement the self-attention mechanism and Transformer architectures such as BERT and GPT; explore generative deep learning using Autoencoders and GANs; and build an end-to-end final project that compares Dense networks, LSTMs, and fine-tuned BERT models. This path provides a deep, practical understanding of how these concepts are applied in real-world scenarios and prepares you for professional work in deep learning.
What you will learn in Hands-on Deep Learning with PyTorch: From Math to Modern AI:
- Understand the foundations of Deep Learning, from basic math to modern architectures like Transformers and Diffusion models.
- Master PyTorch basics, including tensor manipulation, GPU acceleration, and creating neural networks from scratch.
- Grasp the essential math behind AI, including linear algebra, calculus, derivatives, and the chain rule for backpropagation.
- Build, train, and optimize deep neural networks using techniques like Dropout, Batch Normalization, and Adam optimizers.
- Develop Convolutional Neural Networks (CNNs) for image classification and apply Transfer Learning with pre-trained models like ResNet.
- and …
Course specifications
Publisher: Udemy
Instructors: Yogesh Raheja
Language: English
Level: Introductory to Advanced
Number of Lessons: 94
Duration: 10 hours and 50 minutes
Course topics
Hands-on Deep Learning with PyTorch: From Math to Modern AI Prerequisites
No prior deep learning experience needed
Basic understanding of Python programming is required
High-school level mathematics is helpful, though core concepts are reviewed in the course
A Google account to access Google Colab (no local GPU required)
Pictures
Hands-on Deep Learning with PyTorch: From Math to Modern AI introduction video
Installation guide
After Extract, watch with your favorite Player.
Subtitle: None
Quality: 1080p
Downloadly link
Rapidgator link
File password (s): www.downloadly.ir
Size
5 GB