What will you learn in Introduction to Neural Networks and PyTorch Course
Understand the architecture and operation of deep neural networks.
Build and train deep learning models using PyTorch.
Apply activation functions, loss functions, and optimizers effectively.
Use convolutional neural networks (CNNs) for image classification tasks.
Program Overview
Module 1: Introduction to Deep Learning and PyTorch
⏱️ 1 week
Topics: Overview of neural networks, PyTorch setup, tensors
Hands-on: Tensor operations, PyTorch basics
Module 2: Building Neural Networks with PyTorch
⏱️ 1 week
Topics: Model architecture, forward/backward pass, model training
Hands-on: Build and train a simple feedforward neural network
Module 3: Activation and Loss Functions
⏱️ 1 week
Topics: Sigmoid, ReLU, Tanh, cross-entropy, MSE
Hands-on: Experiment with different activation/loss functions
Module 4: Optimization and Backpropagation
⏱️ 1 week
Topics: Gradient descent, backpropagation, optimizers
Hands-on: Implement SGD and Adam for model optimization
Module 5: Convolutional Neural Networks (CNNs)
⏱️ 1 week
Topics: Convolutional layers, pooling, CNN architecture
Hands-on: Build and train a CNN for image recognition
Module 6: Model Evaluation and Deployment
⏱️ 1 week
Topics: Evaluation metrics, overfitting, saving models
Hands-on: Model evaluation and serialization
Get certificate
Job Outlook
High demand for deep learning engineers and AI practitioners.
Average salary ranges from $90K–$150K+ depending on role and location.
Skills in PyTorch are sought after in computer vision, NLP, and ML research.
Specification: Introduction to Neural Networks and PyTorch
|
