adarshkuthuru / deep-learning

Code from the Deep Learning Specialization Assignments run on my local machine

Deep Learning Specialization on Coursera (offered by deeplearning.ai)

This repository showcases the programming assignments from all the courses in the esteemed Deep Learning Specialization on Coursera, which is offered by deeplearning.ai. The content presented here is solely the work of deeplearning.ai and Coursera.

Credits

This repository contains my personal work for this specialization. The code base, quiz questions and diagrams utilized in the assignments are derived from the original material of the Deep Learning Specialization on Coursera, unless specifically mentioned otherwise.

Courses

The Deep Learning Specialization on Coursera comprises five comprehensive courses:

• Course 1: Neural Networks and Deep Learning
• Course 2: Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization
• Course 3: Structuring Machine Learning Projects
• Course 4: Convolutional Neural Networks
• Course 5: Sequence Models

Specialization Information

The Deep Learning Specialization serves as a fundamental program that equips participants with a deep understanding of the capabilities, challenges, and implications of deep learning. It prepares individuals to actively contribute to the development of cutting-edge AI technology. Throughout this specialization, participants will learn to construct and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Transformers. Moreover, they will gain insights into enhancing these models using techniques like Dropout, BatchNorm, Xavier/He initialization, and more. The program delves into theoretical concepts and their practical applications in various industries using Python and TensorFlow. Real-world scenarios, including speech recognition, music synthesis, chatbots, machine translation, and natural language processing, are explored. By undertaking this specialization, participants take a definitive step forward in the realm of AI and acquire the necessary knowledge and skills to advance their careers. Along the way, they receive valuable career guidance from deep learning experts hailing from both industry and academia.

Applied Learning Project

Upon completion, you will be able to:

• Develop and train deep neural networks

• Implement vectorized neural networks

• Identify key architectural parameters

• Apply deep learning techniques to various applications

• Employ best practices for training and validating deep learning applications

• Analyze bias and variance in model performance

• Utilize standard neural network techniques and optimization algorithms

• Implement neural networks using TensorFlow

• Utilize strategies to minimize errors in machine learning systems

• Understand complex machine learning settings

• Apply end-to-end, transfer, and multi-task learning

• Build and train Convolutional Neural Networks

• Apply CNNs to visual detection and recognition tasks

• Utilize neural style transfer for generating art

• Apply these algorithms to image, video, and other 2D/3D data

• Construct and train Recurrent Neural Networks and their variants, such as GRUs and LSTMs

• Apply RNNs to character-level language modeling

• Work with Natural Language Processing (NLP) and Word Embeddings

• Utilize HuggingFace tokenizers and transformers for Named Entity Recognition and Question Answering tasks

Learning Objectives

Throughout the Deep Learning Specialization, you will gain proficiency in the following areas:

• Building and training deep neural networks

• Identifying key architectural parameters

• Implementing vectorized neural networks and applying deep learning techniques to various applications

• Training and validating deep learning models

• Analyzing variance in model performance

• Utilizing standard neural network techniques and optimization algorithms

• Building neural networks using TensorFlow

• Constructing Convolutional Neural Networks (CNNs)

• Applying CNNs to detection and recognition tasks

• Utilizing neural style transfer for generating artistic content

• Applying algorithms to image and video data

• Building and training Recurrent Neural Networks (RNNs)

• Working with Natural Language Processing (NLP) and Word Embeddings

• Utilizing HuggingFace tokenizers and transformer models for Named Entity Recognition (NER) and Question Answering tasks

Usage

I am sharing the assignment notebooks for reference purposes only. These notebooks contain my own pre-filled code and code contributed by other individuals, structured according to the course and week. Please note that the assignment notebooks are subject to change over time.

Join the Slack Workspace and Connect with Mentors and Peers!

Upon enrolling in the course, you are cordially invited to join the dedicated Slack workspace for this specialization. To join, simply follow this link: deeplearningai-nlp.slack.com. The Slack workspace encompasses all courses within this specialization, providing a valuable platform for interaction and collaboration.

Certificate

1. Neural Networks and Deep Learning
2. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization
3. Structuring Machine Learning Projects
4. Convolutional Neural Networks
5. Sequence Models
6. Deep Learning Specialization (Final Certificate)

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References

1. Neural Networks and Deep Learning
2. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization
3. Structuring Machine Learning Projects
4. Convolutional Neural Networks
5. Sequence Models

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📝 License

This work is based on the original content of "Deeplearning.ai" and "Coursera" and is made available under the terms of the MIT license.

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Disclaimer

I fully acknowledge the effort individuals put into developing intuition, comprehending new concepts, and troubleshooting assignments. The solutions provided in this repository are strictly for reference purposes. They are intended to assist individuals in overcoming obstacles they may encounter during their learning journey. Please refrain from copying any part of the code as-is, as the programming assignments are relatively straightforward if the instructions are diligently followed. Similarly, I encourage you to attempt the quizzes independently before consulting the provided solutions.