Deep Learning with PyTorch Training Logo

Deep Learning with PyTorch Training

Live Online & Classroom Enterprise Training

Covers the fundamentals of neural networks, model building, and training using PyTorch. Focuses on CNNs, RNNs, and optimization techniques for real-world AI tasks.

Looking for a private batch ?

REQUEST A CALLBACK

Need help finding the right training?

Your Message

  • Enterprise Reporting

  • Lifetime Access

  • CloudLabs

  • 24x7 Support

  • Real-time code analysis and feedback

What is Deep Learning with PyTorch Course about?

Deep learning powers modern AI applications, from image recognition to natural language processing. This course introduces learners to PyTorch, a flexible and widely used deep learning framework. Participants will learn the fundamentals of neural networks, model building, optimization, and training. Through hands-on projects, learners will explore real-world applications such as computer vision, NLP, and generative AI. By the end, participants will have the skills to develop and deploy deep learning models using PyTorch for academic, research, or enterprise use.

What are the objectives of Deep Learning with PyTorch Course ?

  • At the end of this training, you will be able to: 
  • Understand the fundamentals of deep learning and neural networks. 
  • Build and train models using PyTorch tensors and autograd. 
  • Implement convolutional and recurrent neural networks. 
  • Optimize models with techniques like regularization and transfer learning. 
  • Apply PyTorch to real-world use cases in vision, NLP, and AI applications.

Who is Deep Learning with PyTorch Course for?

  • Data Scientists and Machine Learning Engineers. 
  • AI Researchers and Developers. 
  • Software Engineers transitioning into AI/ML. 
  • Students pursuing careers in Artificial Intelligence. 
  • Professionals aiming to apply deep learning in real-world projects.

What are the prerequisites for Deep Learning with PyTorch Course?

Prerequisites:  

  • Basic understanding of Python programming. 
  • Familiarity with linear algebra and probability. 
  • Knowledge of machine learning fundamentals. 
  • Experience with NumPy or data manipulation libraries. 
  • Curiosity about AI and hands-on experimentation. 

Learning Path: 

  • Introduction to Deep Learning and PyTorch Basics 
  • Tensors, Autograd, and Building Neural Networks 
  • Training, Optimization, and Regularization Techniques 
  • CNNs, RNNs, and Transfer Learning in PyTorch 
  • Real-World Applications and Model Deployment. 

Related Courses: 

  • Machine Learning with Python 
  • Applied Deep Learning with TensorFlow 
  • Natural Language Processing with PyTorch 
  • Computer Vision Fundamentals

Available Training Modes

Live Online Training

2 Days

Course Outline Expand All

Expand All

  • Course Introduction
  • Logistic Regression Cross Entropy Loss
  • Softmax
  • Softmax Function: Using Lines to Classify Data Prediction
  • Softmax PyTorch
  • What's a Neural Network?
  • More Hidden Neurons
  • Neural Networks with Multiple Dimensional Input
  • Multi-Class Neural Networks
  • Backpropagation
  • Activation Functions
  • Deep Neural Networks
  • Deeper Neural Networks: nn.ModuleList()
  • Dropout
  • Neural Network initialization Weights
  • Gradient Descent with Momentum
  • Batch Normalization
  • Convolution
  • Activation Functions and Max Polling
  • Multiple Input and Output Channels
  • Convolutional Neural Network
  • Convolutional Neural Network for MNIST
  • Torch Vision Models
  • Graphics Processing Unit

Who is the instructor for this training?

The trainer for this Deep Learning with PyTorch Training has extensive experience in this domain, including years of experience training & mentoring professionals.

Reviews