Vinayak Sharma

Brief Information

The Deep Learning Specialization was created and is taught by Dr. Andrew Ng, a global leader in AI and co-founder of Coursera. In addition to the lectures and programming assignments, you will also watch exclusive interviews with many Deep Learning leaders. They will share with you their personal stories and give you career advice.

• CNN models
• RNN models
• Hyperparameter tuning

Main Field : Deep Learning General

Brief Information

This course provides a broad introduction to machine learning. Topics include:

• Supervised learning - parametric/non-parametric algorithms, support vector machines, kernels, neural networks
• Unsupervised learning - clustering, dimensionality reduction, recommender systems, deep learning
• Best practices in machine learning - bias/variance theory; innovation process in machine learning and AI

The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms in many cutting edge practical circumstances.

Main Field : Machine Learning

Brief Information

This course covers a wide range of tasks in Natural Language Processing from basic to advanced: sentiment analysis, summarization, dialogue state tracking, to name a few. Upon completing, you will be able to recognize NLP tasks in your day-to-day work, propose approaches, and judge what techniques are likely to work well. The final project is devoted to one of the most hot topics in today’s NLP. You will build your own conversational chat-bot that will assist with search on StackOverflow website. The project will be based on practical assignments of the course, that will give you hands-on experience with such tasks as text classification, named entities recognition, and duplicates detection.

Main Field : NLP

Brief Information

In this final course, you’ll explore four different scenarios you’ll encounter when deploying models. You’ll be introduced to TensorFlow Serving, a technology that lets you do inference over the web. You’ll move on to TensorFlow Hub, a repository of models that you can use for transfer learning. Then you’ll use TensorBoard to evaluate and understand how your models work, as well as share your model metadata with others. Finally, you’ll explore federated learning and how you can retrain deployed models with user data while maintaining data privacy.

Main Field : Production Level Deep Learning (MLOps)

Brief Information

This course teaches you how to use a suite of tools in TensorFlow to more effectively leverage data and train your model. You’ll learn how to leverage built-in datasets with just a few lines of code, use APIs to control how you split your data, and process all types of unstructured data.

I personally incorporated my learning in this course into my internship at Myelin Foundry to test viability of project deployment in Tensorflow.js for web browser based inference. This was done for multiple projects.

Main Field : Data Pipelines for Deep Learning

Brief Information

In the first course of this specialization, we will recap what was covered in the Machine Learning with TensorFlow on Google Cloud Platform Specialization. So, this course is set up as a workshop and in this workshop, you will do End-to-End Machine Learning with TensorFlow on Google Cloud Platform. Deployment of models using GCP is essential for scaling performance.

Main Field : Cloud Pipelines for Models (MLOps)

Brief Information

This course teaches you learn how to train and run machine learning models in any browser using TensorFlow.js. You’ll learn techniques for handling data in the browser, and at the end you’ll build a computer vision project that recognizes and classifies objects from a webcam.

I personally incorporated my learning in this course into my internship at Myelin Foundry to test viability of project deployment in Tensorflow.js for web browser based inference. This was done for multiple projects.

Main Field : Web Deployment

Brief Information

In this 1.5-hour long project-based course, you will learn to create a custom callback function in Keras and use the callback during a model training process. We will implement the callback function to perform three tasks: Write a log file during the training process, plot the training metrics in a graph during the training process, and reduce the learning rate during the training with each epoch.

Main Field : Custom Models

Brief Information

This course will give you an overview of client-side web UI frameworks, in particular Bootstrap 4. You will learn about grids and responsive design, Bootstrap CSS and JavaScript components. You will learn about CSS preprocessors, Less and Sass. You will also learn the basics of Node.js and NPM and task runners like Grunt and Gulp. At the end of this course, you will be able to a)Set up, design and style a web page using Bootstrap 4 and its components, b) Create a responsive web page design, and c) Make use of web tools to setup and manage web sites. This course also includes an honors track that enables you to work on your own project developing a website using Bootstrap 4. Students enrolling in this course should have prior good working knowledge of HTML, CSS and JavaScript.

Main Field : Web Development

Brief Information

This one-week, accelerated online class equips students to containerize workloads in Docker containers, deploy them to Kubernetes clusters provided by Google Kubernetes Engine, and scale those workloads to handle increased traffic. Students also learn how to continuously deploy new code in a Kubernetes cluster to provide application updates.

Main Field : DevOps