What will you learn in Introduction to Neural Networks and PyTorch Course
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Understand the architecture and operation of deep neural networks.
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Build and train deep learning models using PyTorch.
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Apply activation functions, loss functions, and optimizers effectively.
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Use convolutional neural networks (CNNs) for image classification tasks.
Program Overview
Module 1: Introduction to Deep Learning and PyTorch
1 week
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Topics: Overview of neural networks, PyTorch setup, tensors
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Hands-on: Tensor operations, PyTorch basics
Module 2: Building Neural Networks with PyTorch
1 week
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Topics: Model architecture, forward/backward pass, model training
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Hands-on: Build and train a simple feedforward neural network
Module 3: Activation and Loss Functions
1 week
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Topics: Sigmoid, ReLU, Tanh, cross-entropy, MSE
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Hands-on: Experiment with different activation/loss functions
Module 4: Optimization and Backpropagation
1 week
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Topics: Gradient descent, backpropagation, optimizers
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Hands-on: Implement SGD and Adam for model optimization
Module 5: Convolutional Neural Networks (CNNs)
1 week
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Topics: Convolutional layers, pooling, CNN architecture
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Hands-on: Build and train a CNN for image recognition
Module 6: Model Evaluation and Deployment
1 week
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Topics: Evaluation metrics, overfitting, saving models
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Hands-on: Model evaluation and serialization
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Job Outlook
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High demand for deep learning engineers and AI practitioners.
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Average salary ranges from $90K–$150K+ depending on role and location.
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Skills in PyTorch are sought after in computer vision, NLP, and ML research.
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Related Courses
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Neural Networks and Deep Learning Course – Build a strong understanding of fundamental neural network architectures and learn how they power today’s AI systems.
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Convolutional Neural Networks Course – Dive into image recognition, feature extraction, and CNN-based deep learning models used in computer vision.
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Introduction to Deep Learning & Neural Networks with Keras Course – Learn how to design, train, and evaluate deep learning models using the beginner-friendly Keras framework.
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Last verified: March 12, 2026
