Deep Learning Specialization

Issued by: DeepLearning.AI & Coursera
Instructors: Andrew Ng, Kian Katanforoosh, Younes Bensouda Mourri
Completed on: June 23, 2024

Specialization Overview:
This program, created by AI pioneer Andrew Ng, provides a comprehensive pathway into the world of deep learning. It is designed to equip learners with the knowledge and skills to understand the capabilities, challenges, and consequences of deep learning and to participate in the development of leading-edge AI technology.

The specialization covers the core concepts of deep learning, from foundational neural networks to advanced architectures. I gained hands-on experience building and training various neural network models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs, and Transformers. The curriculum also emphasizes practical industry techniques for improving model performance, such as Dropout, BatchNorm, and Xavier/He initialization, all implemented using Python and TensorFlow. Through a series of real-world case studies, I applied these concepts to tasks like speech recognition, music synthesis, chatbots, machine translation, and natural language processing.

Key Skills Acquired:

• Foundational Concepts: Artificial Neural Networks, Deep Learning, Backpropagation, Optimization, Hyperparameter Tuning
• Core Architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers
• Advanced Models: Gated Recurrent Unit (GRU), Long Short Term Memory (LSTM), Attention Models
• Frameworks & Programming: TensorFlow, Python Programming
• Applications: Object Detection and Segmentation, Facial Recognition Systems, Natural Language Processing (NLP), Transfer Learning, Multi-Task Learning

Curriculum:
The specialization is composed of five in-depth courses:

1. Neural Networks and Deep Learning: Building a solid foundation in deep learning and implementing vectorized neural networks.
2. Improving Deep Neural Networks: Mastering hyperparameter tuning, regularization, and optimization to build robust models.
3. Structuring Machine Learning Projects: Learning best practices for developing and analyzing machine learning systems, including bias/variance analysis.
4. Convolutional Neural Networks: Building and applying CNNs for visual detection, recognition, and artistic style transfer.
5. Sequence Models: Working with RNNs, LSTMs, and Transformers for NLP, language modeling, and other sequence-based tasks.

This specialization has provided me with a strong theoretical and practical foundation in deep learning, preparing me to tackle complex AI challenges and contribute to innovative solutions in the field.

You can view the certificate here.