Udemy – Complete Deep Learning Bootcamp with PyTorch 2026-10

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Descriptions

Complete Deep Learning Bootcamp with PyTorch, PyTorch Fundamentals: Tensors, computational graphs, Autograd, backpropagation, training loops, nn.Module, loss functions, optimizers, Dataset, and DataLoader. Neural Networks: Weights, biases, forward and backward propagation, activation functions, gradient descent, optimization, weight initialization, and loss functions. Regularization & Training: L1/L2 regularization, Dropout, Batch Normalization, Layer Normalization, Instance Normalization, and Early Stopping. Convolutional Neural Networks: Convolution, kernels, feature maps, padding, stride, pooling, CNN architectures, and data augmentation. Modern CNN Architectures: VGG, ResNet, DenseNet, MobileNet, and Transfer Learning. Computer Vision: Image classification, Grad-CAM, object detection, YOLO, and advanced image segmentation techniques. Image Segmentation: U-Net, Mask R-CNN, FCN, SegNet, DeepLab, PSPNet, HRNet, BiSeNet, SegFormer, and Transformer-based segmentation. Sequence Models: RNN, LSTM, GRU, Bidirectional RNNs, BiLSTM, BiGRU, Stacked RNNs, and Seq2Seq. Attention Mechanisms: Bahdanau Attention, Luong Attention, Global & Local Attention, and Self-Attention. Transformers: QKV, Multi-Head Attention, Transformer Architecture, Encoder, Decoder, Add & Norm, and Feed-Forward Networks.

Hands-On Projects: Build practical Deep Learning projects using PyTorch, real-world datasets, and modern Deep Learning architectures. Practical Learning: Learn through concepts, mathematics, implementation, coding, model training, and evaluation. By the end of this course, you will have a strong foundation in Deep Learning and practical experience building real-world neural networks and applications, computer vision systems, sequence models, and Transformer-based solutions using PyTorch.

What you’ll learn

Build and train deep learning models with PyTorch, including tensors, Autograd, neural networks, loss functions, and optimizers.
Understand and implement neural networks, CNNs, RNNs, LSTMs, GRUs, and modern Transformer architectures.
Apply deep learning to real-world computer vision tasks, including image classification, object detection, and image segmentation.
Build hands-on deep learning projects using PyTorch, transfer learning, YOLO, Grad-CAM, and advanced vision models.
Understand attention, self-attention, QKV, multi-head attention, and the architecture of Transformer models.
Apply optimization, regularization, normalization, weight initialization, and training techniques to improve model performance.
Build sequence-based NLP solutions using RNN, LSTM, GRU, Seq2Seq, and attention mechanisms.
Develop and evaluate end-to-end deep learning solutions using real-world datasets and practical model-building workflows.

Who this course is for

Aspiring Data Scientists and Machine Learning Engineers who want to learn Deep Learning with PyTorch.
Python developers who want to transition into Deep Learning and AI.
Machine Learning professionals who want to strengthen their Deep Learning and Computer Vision skills.
AI/ML students and beginners who want a structured, hands-on path from neural-network fundamentals to advanced architectures.
Developers and professionals interested in Computer Vision, NLP, RNNs, LSTMs, Transformers, and Generative AI foundations.
Learners preparing for Deep Learning and AI interviews who want both conceptual understanding and practical implementation experience.
Anyone who wants to build real-world Deep Learning projects using PyTorch, from model development through practical deployment.

Specificatoin of Complete Deep Learning Bootcamp with PyTorch

Publisher : Udemy
Teacher : EduMentor Deepti
Language : English
Level : All Levels
Number of Course : 110
Duration : 43 hours and 44 minutes

Content of Complete Deep Learning Bootcamp with PyTorch

Complete Deep Learning Bootcamp with PyTorch

Requirements

Basic Python programming knowledge is recommended. Familiarity with variables, functions, loops, and basic data structures is helpful.
Basic understanding of Machine Learning concepts such as training, testing, features, labels, and model evaluation is recommended.
Basic knowledge of NumPy and Pandas will be helpful when working with datasets and implementing practical projects.
No prior PyTorch or Deep Learning experience is required. The course starts with PyTorch fundamentals and builds progressively.
A computer capable of running Python and PyTorch is required. A GPU is helpful for larger projects but is not mandatory for learning the concepts.