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 Running a Forward Pass in Neural Networks 16:34 Binary and Multi-Class Classification in PyTorch 18:25 Introduction to Loss Functions 21:12 Understanding One-Hot Encoding 23:05 Cross-Entropy Loss for Classification 24:42 Backpropagation and Gradient Descent 26:06 Implementing Backpropagation in PyTorch 27:58 Understanding Optimizers in Deep Learning 29:23 Data Loading and Preparation in PyTorch 31:57 Setting Up the Training Loop 33:06 Training a Regression Model in PyTorch 37:23 Vanishing Gradients and Activation Functions 39:08 ReLU and Leaky ReLU Activation Functions 40:51 Learning Rate and Momentum in Optimization 44:25 Techniques to Improve Model Performance 46:05 Transfer Learning and Fine-Tuning Models 47:37 Evaluating Models with Training and Validation Data 50:35 Accuracy, Precision, and Recall Metrics 52:18 Techniques to Reduce Overfitting 55:23 A General Strategy for Deep Learning Projects 58:55 Course Summary and Next Steps 1:00:31 Advanced PyTorch: Object-Oriented Programming 1:02:35 Handling Tabular Data with PyTorch 1:05:11 Training and Evaluating Models in PyTorch 1:08:09 Solving Gradient Instability Issues 1:12:48 Deep Learning with Image Data 1:16:07 Data Augmentation for Image Classification 1:19:37 Building Convolutional Neural Networks 1:24:24 Evaluating CNNs and Performance Metrics 1:28:40 Introduction to Recurrent Neural Networks (RNNs) 1:32:10 Training and Evaluating RNNs 1:36:27 LSTMs and GRUs for Long-Term Dependencies 1:42:11 Forecasting with Time-Series Data 1:46:42 Multi-Input and Multi-Output Models 1:51:14 Loss Weighting and Model Evaluation 1:54:05 Final Course Summary and Future Learning Paths 🖇️ Resources & Documentation Check out our newly released newsletter on Substack — The Median: https://dcthemedian.substack.com Introduction to Deep Learning with PyTorch: https://www.datacamp.com/courses/introduction-to-deep-learning-with-pytorch Intermediate Deep Learning with PyTorch: https://www.datacamp.com/courses/intermediate-deep-learning-with-pytorch Career Track - Associate AI Engineer for Data Scientists: https://www.datacamp.com/tracks/associate-ai-engineer-for-data-scientists Tutorial - PyTorch CNN Tutorial: Build and Train Convolutional Neural Networks in Python: https://www.datacamp.com/tutorial/pytorch-cnn-tutorial Tutorial - PyTorch Lightning: A Comprehensive Hands-On Tutorial: https://www.datacamp.com/tutorial/pytorch-lightning-tutorial 📱 Follow Us for More AI & Data Science Content Facebook: https://www.facebook.com/datacampinc/ Twitter: https://twitter.com/datacamp LinkedIn: https://www.linkedin.com/school/datacampinc/ Instagram: https://www.instagram.com/datacamp/ #PyTorch #DeepLearning #AI #MachineLearning #NeuralNetworks #LSTM #CNN #MultiInputOutput #DataScience #AIModels #GradientDescent #Optimization #ArtificialIntelligence