Mastering PyTorch Deep Learning & Generative AI & CNN & SNN
Published 9/2026
Created by Neelam Jain Tueseon Technologies
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 182 Lectures ( 9h 0m ) | Size: 3.7 GB
Learn PyTorch, CNNs, Transformers, Hugging Face, GANs, Diffusion Models, Model Deployment, Spiking Neural Networks SNN
What you’ll learn
Build deep learning models from scratch using PyTorch.
Understand tensors, autograd, neural networks, and backpropagation with practical examples.
Develop Computer Vision applications using CNNs, Transfer Learning, and pretrained models.
Learn RNNs, LSTMs, GRUs, Attention Mechanisms, and Transformer architectures.
Fine-tune Hugging Face Transformer models such as BERT and RoBERTa for real-world NLP tasks.
Create Generative AI models using Autoencoders, VAEs, GANs, and Diffusion Models.
Optimize, evaluate, and deploy AI models using TorchScript, ONNX, FastAPI, and Docker.
Explore the fundamentals of Spiking Neural Networks (SNNs) and Neuromorphic AI.
Requirements
Basic knowledge of Python programming is recommended.
No prior experience with Deep Learning or PyTorch is required.
A computer running Windows, macOS, or Linux with internet access.
Willingness to install free software such as Python, PyTorch, VS Code, and Jupyter Notebook.
A curiosity to learn Artificial Intelligence through hands-on coding.
No GPU is required to complete the course, although one can speed up model training.
Familiarity with basic mathematics (high school level) is helpful but not mandatory.
Enthusiasm to practice coding exercises and build real-world AI projects.
Description
Welcome toMastering PyTorch: Deep Learning & Generative AI, a complete hands-on course designed to help you build a strong foundation in modern Artificial Intelligence using one of the world’s most popular deep learning frameworks—PyTorch.
Whether you’re a student, software developer, machine learning engineer, data scientist, researcher, or AI enthusiast, this course will take you step by step from the fundamentals of Deep Learning to advanced topics used in today’s AI industry. You don’t need prior experience with deep learning—only basic Python programming knowledge is recommended.
We begin by understandingArtificial Intelligence, Machine Learning, and Deep Learningbefore diving into PyTorch fundamentals, tensor operations, automatic differentiation (Autograd), and buildingArtificial Neural Networks (ANNs). You’ll then learn industry best practices for training deep learning models, including DataLoaders, optimization techniques, regularization, batch normalization, dropout, hyperparameter tuning, and model evaluation.
As you progress, you’ll build powerful Computer Vision applications usingConvolutional Neural Networks (CNNs), learn transfer learning with popular pretrained models like ResNet, VGG, EfficientNet, MobileNet, and integrate OpenCV into your workflows.
The course then explores Sequential Neural Networks, includingRNNs, LSTMs, and GRUs, before moving into one of the most important innovations in AI—the Transformer Architecture. You’ll understand Attention Mechanisms, Self-Attention, Multi-Head Attention, Vision Transformers, and work with the Hugging Face ecosystem, including pretrained models, datasets, tokenizers, and inference pipelines.
Next, you’ll learn how to fine-tune state-of-the-art Transformer models such as BERT, DistilBERT, and RoBERTa for your own applications. We then move into the exciting world ofGenerative AI by exploring Autoencoders, Variational Autoencoders (VAE),Generative Adversarial Networks (GANs), Diffusion Models, Stable Diffusion concepts, CLIP, LoRA, DreamBooth, and modern image generation techniques.
Beyond model development, this course also teaches how to deploy AI applications using FastAPI, Docker, TorchScript, ONNX, model optimization, quantization, pruning, mixed precision training, and production best practices.
You’ll exploreSpiking Neural Networks (SNNs) and Neuromorphic AI, an emerging field inspired by the human brain that represents the next generation of energy-efficient artificial intelligence.
Every concept is explained using simple English and practical coding examples. Throughout the course, you’ll write code, build models, train networks, evaluate performance, and gain the confidence to create real-world AI solutions using PyTorch.
By the end of this course, you’ll be able to design, train, fine-tune, optimize, and deploy deep learning models while understanding the technologies behind modern AI systems such as ChatGPT, computer vision applications, intelligent recommendation systems, and generative AI models.
Join today and start your journey toward becoming a confident Deep Learning and PyTorch developer.
Who this course is for
Students who want to start a career in Artificial Intelligence and Deep Learning.
Python developers who want to transition into Machine Learning and AI.
Software engineers looking to build AI-powered applications using PyTorch.
Data Scientists who want practical experience with modern deep learning frameworks.
Machine Learning Engineers who want to master PyTorch and Generative AI.
Researchers and professionals interested in Computer Vision, NLP, and Foundation Models.
AI enthusiasts who want to understand how technologies like ChatGPT and image generation models work.
Anyone interested in learning modern Deep Learning from beginner to advanced level through practical examples.
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