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.
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 and Their Structure
02:12 Why PyTorch?
02:49 Introduction to Tensors in PyTorch
03:25 Tensor Operations and Properties
04:02 Matrix Multiplication in PyTorch
04:38 Building a Simple Neural Network
05:15 Fully Connected Layers Explained
07:13 Understanding Weights and Biases
08:23 Example: Weather Prediction Model
08:59 Adding Hidden Layers to a Neural Network
10:14 Fully Connected Networks and Model Capacity
11:34 Calculating Learnable Parameters
12:07 Introduction to Activation Functions
12:42 Sigmoid and Softmax for Classification
14:38 Forward Pass in Neural Networks
16:34 Understanding Loss Functions
18:25 Cross-Entropy Loss and One-Hot Encoding
20:34 Backpropagation and Gradient Descent
24:09 Optimizing Model Training with PyTorch
27:58 Training Deep Learning Models with Data
31:57 Building a PyTorch Training Loop
33:49 Understanding Learning Rate and Momentum
37:23 Vanishing Gradients and Activation Functions
39:08 ReLU and Leaky ReLU
40:51 The Role of Optimizers in Training
44:25 Model Evaluation and Overfitting
50:01 Measuring Accuracy and Loss
52:18 Strategies to Prevent Overfitting
55:23 Recipe for Training Deep Learning Models
58:55 Conclusion and Next Steps in Deep Learning

🖇️ Resources & Documentation

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[PriscellaBamby]

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