Introduction to Deep Learning & Neural Networks with Keras

🎓 Certificate #

Provider: IBM (via Coursera)
Verified Certificate: View on Coursera

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📘 Course Overview #

This course is part of the IBM AI Engineering Professional Certificate and provides a strong foundation in deep learning, focusing on the practical implementation of neural networks using Keras.

Topics covered include:

• Fundamentals of artificial neural networks (ANNs)
• Forward and backward propagation
• Gradient descent and optimization
• Activation functions (Sigmoid, ReLU, Softmax, etc.)
• Dealing with vanishing gradients
• Keras Sequential vs Functional API
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Transformers
• Autoencoders
• End-to-end projects with image classification and captioning

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🧪 Hands-On Labs & Projects #

Module 1: Foundations #

• Implemented a simple neural network forward pass manually (NumPy).
• Learned how weights, biases, and activation functions produce outputs.

Module 2: Training Neural Networks #

• Backpropagation Lab: Solved the XOR problem step-by-step.
• Activation Functions Lab: Explored Sigmoid vs ReLU, and vanishing gradient effects.

Module 3: Keras Basics #

• Built models using Sequential and Functional API.
• Applied classification (Car dataset) and regression (Concrete dataset).
• Learned how .fit() automates forward/backward propagation.

Module 4: Advanced Architectures #

• CNN Lab: Implemented image classification with Conv2D, MaxPooling, Flatten, Dense.
• Transformers Lab: Implemented self-attention, cross-attention, and transformer blocks.
• RNNs: Learned how hidden states retain sequential memory.

Module 5: Final Project #

• Aircraft Damage Classification & Captioning
  • Used VGG16 as a pretrained feature extractor for binary classification (dent vs crack).
  • Built a custom Keras layer integrating BLIP (Transformer) for captioning & summarization.
  • Achieved ~84% test accuracy on classification + generated natural-language summaries.
  • Final Project Notebook

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📊 Key Learnings #

• Transfer learning with pretrained CNNs for efficient classification.
• Building and training models with Keras (Sequential & Functional).
• Handling overfitting with dropout, data augmentation, early stopping.
• Integrating transformer-based models for image captioning.
• Creating custom Keras layers with TensorFlow.
• Combining structured outputs (labels) with natural language explanations (captions).

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📌 Tools & Libraries #

• TensorFlow & Keras
• NumPy & Matplotlib
• Hugging Face Transformers
• PIL (Image Processing)

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• Issued by: IBM
• Verified by: Coursera
• Credential: Verify Certificate

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🔗 Next Steps #

This course set the stage for:

• Advanced Deep Learning (CNNs, RNNs, Transformers)
• Generative AI applications
• Integration of deep learning in real-world projects like Trackr and NeuroFocus

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🏆 Completion #

This repository serves as a portfolio-ready record of my work in Deep Learning with Keras, demonstrating both foundational understanding and practical implementations through labs and projects.