📄 About

The Deep Learning Specialization offered by DeepLearning.AI is a series of 5 online courses designed to provide a comprehensive introduction to the field of deep learning. Created by Andrew Ng, a prominent figure in the field of artificial intelligence, this specialization covers a range of topics related to deep learning and neural networks.

This repository contains documentation and resources used to complete the certification, relevant notes and other code snippets, and proof of certification for each course.

📑 Courses

✅ 01. Neural Networks and Deep Learning
- Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning (DL) to applications
✅ 02. Improving Deep Neural Networks
- Train test sets, analyze variance for deep learning applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow
✅ 03. Structuring Machine Learning Project
- Use strategies for reducing errors in machine learning systems, understand complex machine learning settings, and apply end-to-end, transfer, and multi-task learning.
✅ 04. Convolutional Neural Networks (CNNs)
- Build a convolutional neural network (CNN) and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data
✅ 05. Sequence Models
- Build and train recurrent neural network (RNN), work with natural language processing (NLP) and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering

🛠️ Tools

The following tools were used to complete this certification:

📖 Libraries

The following Python libraries were used throughout the certification:

📄 Lecture Notes References

Here are some references of lecture notes and reviews drawn by some communities, authors and editors:

🏆 Certificates