Deep Learning with PyTorch

Unlocking the Power of Neural Networks
Why This Training?
The transformative power of deep learning is being harnessed across numerous industries, from healthcare to finance to entertainment. With PyTorch, this power becomes accessible, flexible, and scalable. This hands-on training will guide you through the intricacies of deep learning, ensuring you're equipped with the skills to develop and deploy powerful neural network models using PyTorch.
Duration: 9 Hours (online / virtual live session)

Who Should Attend?

 Data scientists and machine learning practitioners looking to dive deeper into neural networks..
 Software developers and engineers keen on expanding their AI toolkit.
 Anyone with a curiosity about deep learning and its applications.


 A foundational understanding of Python programming.
 Basic knowledge of machine learning concepts.
 Familiarity with linear algebra and calculus is beneficial but not mandatory.

Course Highlights

 Foundations of Deep Learning: Discover the magic behind neural networks and their real-world implications.
 PyTorch Proficiency: Master the art of building, training, and deploying neural network models using PyTorch.
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 Hands-on Labs: Dive into practical exercises with popular datasets, gaining firsthand experience in tackling real-world challenges.
 Advanced Neural Network Architectures: Learn about CNNs, RNNs, LSTMs, and GANs, understanding their use-cases and implementations.
 From Development to Deployment: Understand the pipeline of taking a PyTorch model and deploying it for real-world applications.

Materials Required by Participants

A computer with a stable internet connection.
Installation of Python (preferably 3.7 or newer) and PyTorch.
An integrated development environment (IDE) like Jupyter Notebook or PyCharm.
Access to platforms like Google Colab can be beneficial for GPU-accelerated training.
Datasets and code templates will be provided during the training.
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Training Content

Deep Learning with PyTorch

1. Introduction to Deep Learning and PyTorch

Objective: Define and contextualize data.
1.1. Deep Learning Overview
  • Why PyTorch?
  • Differences between machine learning and deep learning
  • Real-world applications of deep learning

1.2. PyTorch Introduction
  • Why PyTorch?
  • Tensors: PyTorch's fundamental building block
  • Basic tensor operations
1.3.Neural Networks Basics
  • Perceptrons, activation functions, and layers
  • Feedforward neural networks
1.4.Building a Simple Neural Network with PyTorch
  • PyTorch's nn.Module
  • Constructing a basic network
  • Forward propagation

2. Training Neural Networks and Practical Deep Learning

Objective: Enable participants to train deep learning models using PyTorch and understand techniques for improving model performance.
2.1. Loss Functions and Optimizers
  • Common loss functions in deep learning
  • Introduction to PyTorch's optimization algorithms
2.2. Backpropagation and Training
  • Concept of backpropagation
  • Training loop: forward and backward pass
  • PyTorch's autograd system for automatic differentiation
2.3. Regularization and Dropout
  • Preventing overfitting in deep networks
  • Implementing dropout in PyTorch
2.4. Convolutional Neural Networks (CNNs)
  • Importance of CNNs in image data
  • Building a basic CNN using PyTorch
2.5. Hands-on Lab: Image Classification
  • Using a popular dataset (e.g., CIFAR-10 or Fashion MNIST)
  • Training a CNN to classify images

3. Advanced Topics and Real-World Applications

Objective: Dive deeper into advanced PyTorch modules and showcase the application of deep learning in real-world scenarios.
3.1. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
  • Working with sequential data
  • Basics of RNNs and LSTMs
  • Implementation in PyTorch
3.2. Transfer Learning
  • What is transfer learning?
  • Using pre-trained models in PyTorch
3.3. Generative Adversarial Networks (GANs)
  • Understanding GANs
  • Basic GAN implementation in PyTorch
3.4. Deployment and Production
  • TorchScript and model serialization
  • Serving PyTorch models in production environments
3.4. Hands-on Lab: Real-world Project
  • Implementing a deep learning solution for a realistic problem
  • Tips, tricks, and best practices
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