Deep Learning with PyTorch Full Course | Master PyTorch, Tensors, and Neural Networks
Master Deep Learning with PyTorch! This full-course takes you from the fundamentals to advanced techniques, covering everything from tensors and neural networks to convolutional architectures, sequence models, and multi-input/output deep learning systems. Whether you’re a beginner or looking to refine your PyTorch skills, this comprehensive guide will equip you with the knowledge to build and optimize state-of-the-art AI models.
📌 What You’ll Learn in This Course:
PyTorch Fundamentals: Master tensors, tensor operations, and automatic differentiation.
Building Neural Networks: Learn how to design and train deep learning models using PyTorch’s torch.nn module.
Optimization Techniques: Implement backpropagation, loss functions, and optimizers like SGD and Adam.
Computer Vision with CNNs: Train convolutional neural networks (CNNs) for image classification.
Recurrent Architectures: Build sequence models using RNNs, LSTMs, and GRUs for time-series forecasting.
Handling Multiple Inputs & Outputs: Develop advanced architectures that process multiple inputs and generate multiple outputs.
Overcoming Training Challenges: Solve issues like vanishing gradients, overfitting, and exploding gradients.
Transfer Learning & Fine-Tuning: Leverage pre-trained models to improve performance on new tasks.
📕 Video Highlights
00:00 Introduction to Deep Learning with PyTorch
00:27 Meet Your Instructor
01:06 What is Deep Learning?
01:39 Neural Networks Explained
02:11 Why PyTorch for Deep Learning?
02:48 Introduction to PyTorch Tensors
03:25 Tensor Operations and Matrix Multiplication
04:02 Building a Simple Neural Network
05:15 Understanding Fully Connected Layers
06:37 Weights, Biases, and Their Role
07:45 Neural Networks in Action: Weather Prediction Example
08:23 Adding Hidden Layers with nn.Sequential
09:37 Understanding Model Capacity and Parameter Counts
10:55 Introduction to Activation Functions
12:07 Sigmoid and Softmax Activation Functions
14:38 Runn
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Chapters (16)
Introduction to Deep Learning with PyTorch
0:27
Meet Your Instructor
1:06
What is Deep Learning?
1:39
Neural Networks Explained
2:11
Why PyTorch for Deep Learning?
2:48
Introduction to PyTorch Tensors
3:25
Tensor Operations and Matrix Multiplication
4:02
Building a Simple Neural Network
5:15
Understanding Fully Connected Layers
6:37
Weights, Biases, and Their Role
7:45
Neural Networks in Action: Weather Prediction Example
8:23
Adding Hidden Layers with nn.Sequential
9:37
Understanding Model Capacity and Parameter Counts
10:55
Introduction to Activation Functions
12:07
Sigmoid and Softmax Activation Functions
14:38
Runn
🎓
Tutor Explanation
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