Neural Networks and Deep Learning Fundamentals

Build a strong foundation in Neural Networks and Deep Learning, the core technologies behind modern Artificial Intelligence systems such as image recognition, speech processing, and autonomous systems.
This course is designed to help you understand both the theoretical concepts and practical implementation of deep learning models using industry-standard tools.
You will begin with an introduction to neural networks, understanding how artificial neurons work and how deep learning models are structured.
Next, you will learn forward propagation and activation functions, which define how data flows through a neural network and how decisions are made.
The course then dives into loss functions and backpropagation, the key mechanism that enables neural networks to learn from data and improve accuracy.
You will also explore gradient descent and optimization techniques, essential for training deep learning models efficiently.
On the practical side, you will build neural networks using both:


TensorFlow and Keras (industry-friendly high-level APIs)
PyTorch (widely used in research and advanced AI development)

Finally, you will apply your knowledge in a hands-on deep learning project, building an image classification model using the CIFAR-10 dataset, followed by model evaluation and performance improvement.




🚀 What You’ll Learn:

Fundamentals of neural networks and deep learning
Forward propagation and activation functions
Loss functions and backpropagation
Gradient descent and optimization techniques
Building models using TensorFlow and Keras
Building models using PyTorch
Image classification using CIFAR-10 dataset
End-to-end deep learning workflow




🎯 Who This Course Is For:

Beginners entering AI and Deep Learning
Engineering and data science students
ML professionals moving into deep learning
Anyone interested in neural networks and AI




💡 Why This Course Stands Out:

Covers both theory and hands-on implementation
Includes two major frameworks: TensorFlow & PyTorch
Real-world image classification project
Beginner-friendly yet industry-relevant

External Reference 

TensorFlow
PyTorch

 
Internal Reference
Model Tuning and Optimization for Machine Learning

 

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