This repository contains all the programming assignments and quiz questions that I completed to learn the contents of this specialization. All source code and data displayed in this repository belong to the Deep Learning Specialization on Coursera. The solutions posted are my own and are only meant for reference purposes.
DeepLearning.AI is an education technology company that is empowering the global workforce to build an AI-powered future through world-class education, hands-on training, and a collaborative community.
It was founded in 2017 by machine learning and education pioneer Andrew Ng to fill a need for world-class AI education. DeepLearning.AI has created high-quality AI programs on Coursera that have gained an extensive global following. By providing a platform for education and fostering a tight-knit community, DeepLearning.AI has become the pathway for anyone looking to build an AI career.
Link: https://www.deeplearning.ai
The Deep Learning Specialization is designed as a foundational program aimed at imparting an understanding of the capabilities, challenges, and consequences associated with deep learning. It prepares individuals to engage in the development of cutting-edge AI technology.
Participants in this Specialization have the opportunity to construct and train various neural network architectures, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Transformers. They learn to enhance these networks with techniques such as Dropout, BatchNorm, Xavier/He initialization, among others. The program emphasizes mastering theoretical concepts alongside their practical industry applications, utilizing Python and TensorFlow. It covers tackling real-world scenarios such as speech recognition, music synthesis, chatbots, machine translation, and natural language processing.
With AI revolutionizing multiple industries, the Deep Learning Specialization serves as a conduit for individuals to make a significant move in the AI domain. It aids in acquiring the knowledge and skills essential for advancing one's career. Additionally, participants receive career guidance from deep learning experts in both industry and academia.
Applied Learning Project:
By the conclusion of the program, participants are able to:
- Construct and train deep neural networks, implement vectorized networks, identify architecture parameters, and apply deep learning to various applications.
- Employ best practices in training, developing test sets, and analyzing bias/variance in deep learning applications; utilize standard neural network techniques; apply optimization algorithms; and implement neural networks in TensorFlow.
- Deploy strategies to minimize errors in machine learning systems, navigate complex machine learning settings, and apply concepts like end-to-end, transfer, and multi-task learning.
- Develop a Convolutional Neural Network for visual detection and recognition tasks, use neural style transfer for artistic creation, and apply these algorithms to image, video, and other 2D/3D data.
- Build and train Recurrent Neural Networks and their variants (GRUs, LSTMs), apply these networks to character-level language modeling, engage with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers for Named Entity Recognition and Question Answering.
Link to the specialization on Coursera: https://www.coursera.org/specializations/deep-learning
In the first course of the Deep Learning Specialization, students study the foundational concept of neural networks and deep learning.
By the end, students are familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to their own applications.
The Deep Learning Specialization is their foundational program that helps them understand the capabilities, challenges, and consequences of deep learning and prepares them to participate in the development of leading-edge AI technology. It provides a pathway for them to gain the knowledge and skills to apply machine learning to their work, level up their technical career, and take the definitive step in the world of AI.
Skills: Tensorflow, Deep Learning, Hyperparameter tuning, Mathematical Optimization
Programming assignments
- Week 1 - First week doesn't have any Programming Assignment
- Week 2 - Logistic Regression with a Neural Network mindset
- Week 3 - Planar data classification with one hidden layer
- Week 4 - Building your Deep Neural Network: Step by Step
- Week 4 - Deep Neural Network for Image Classification: Application
In the second course of the Deep Learning Specialization, students open the deep learning black box to understand the processes that drive performance and generate good results systematically.
By the end, students learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; are able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow.
Skills: Gated Recurrent Unit (GRU), Recurrent Neural Network, Natural Language Processing, Long Short Term Memory (LSTM), Attention Models
Programming Assignments
- Week 1 - Initialization
- Week 1 - Regularization
- Week 1 - Gradient Checking
- Week 2 - Optimization Methods
- Week 3 - TensorFlow Tutorial
In the third course of the Deep Learning Specialization, students learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.
By the end, students are able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning.
Skills: Artificial Neural Network, Backpropagation, Python Programming, Deep Learning, Neural Network Architecture
- This third course doesn't have programming assignments.
In the fourth course of the Deep Learning Specialization, students understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more.
By the end, students are able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data.
Skills: Decision-Making, Machine Learning, Deep Learning, Inductive Transfer, Multi-Task Learning
Programming Assignments
- Week 1 - Convolutional Model, Step by Step
- Week 1 - Convolution Model Application
- Week 2 - Residual Networks
- Week 2 - Transfer Learning with MobileNetV2
- Week 3 - Autonomous Driving - Car detection with YOLO
- Week 3 - Image Segmentation with U-Net
- Week 4 - Face Recognition
In the fifth course of the Deep Learning Specialization, students become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more.
By the end, students are able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering.
Skills: Facial Recognition System, Tensorflow, Convolutional Neural Network, Deep Learning, Object Detection and Segmentation
Programming Assignments
- Week 1 - Building a Recurrent Neural Network - Step by Step
- Week 1 - Dinosaur Island-Character-Level Language Modeling
- Week 1 - Jazz improvisation with LSTM
- Week 2 - Operations on Word Vectors - Debiasing
- Week 2 - Emojify
- Week 3 - Neural Machine Translation
- Week 4 - Transformer Network
- Week 4 - Transformer Pre-processing
- Week 4 - Transformer Network Application: Named-Entity Recognition
- Week 4 - Transformer Network Application: Question Answering