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About This Course

Artificial Intelligence has made leaps and bounds in the past few years. Deep Learning, one the most important driving forces of this AI revolution has become an essential technique to tackle complex challenges such as Computer Vision, Natural Language Processing, and Voice Recognition. In this course, you will learn the essential tools and skills required to apply DL to real-life problems.

Why you should take this course?!

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Learn highly in-demand skills

Tensorflow, Keras, PyTorch, GPU

Hands-on labs using cutting-edge tools

Computer Vision, NLP, Forecasting, Recommender Systems

Experienced instructors

Experienced AI Researchers and Data Science Practitioners

Build portfolio projects

Increase the odds of getting hired by building cool projects

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Who this course is for

The Applied Deep Learning course is developed for students and professionals who want to learn applied AI techniques for a career transition. It is an intermediate level course in the Artificial Intelligence track. It prepares learners with the necessary theoretical and technical foundations before getting more specialized. Students are required to have python and basic machine learning knowledge before enrolling in this course.

Learning Outcome

Upon completing the course, students will be able to:

  • 01. In-depth knowledge of DL theory
  • 02. Master Deep Learning tools
  • 03. Apply practical DL use cases
  • 04. Build an awesome AI project portfolio

Gaining in-depth knowledge of neural networks and deep learning techniques

Understanding how neural networks work is the first step towards

  • Neural Networks and backpropagation
  • Different activation functions
  • Various optimization methods
  • Regularization tehniques
  • Batch normalizations

Master industry-leading DL tools and platforms

Practice deep learning techniques using the most popular tools such as Tensorflow 2.0, Keras, PyTorch and learn how to speed up your processing using GPU servers

  • Tensorflow 2.0
  • Keras
  • PyTorch
  • AWS/Azure GPU

Apply deep learning to real-life problems

This course is extremely hands-on. Students will learn how to use Tesnorflow and Pytorch to train deep neural nets to solve various data challenges including image classifications, natural language processing, transfer learning, anomaly detection, recommender systems.

  • Image Classification using Convolutional Neural Networks (CNN)
  • NLP using RNN and LSTM
  • Sentiment Analysis using Transfer Learning
  • Embeddings for Recommender System
  • Autoencoders for Anomaly Detection

Build your AI portfolio under the supervision of our AI mentors

Throughout the course, students will need to complete one deep learning project. Our assistant instructors and AI mentors will provide the guidance and support you need to build something awesome.

  • Receive TA support
  • Guidance from mentors on project ideation
  • Support on technical challenges

Course Resources

http://<iframe src=\"//www.slideshare.net/slideshow/embed_code/key/CxWTSczCFJtllr\" width=\"595\" height=\"485\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" style=\"border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;\" allowfullscreen> </iframe> <div style=\"margin-bottom:5px\"> <strong> <a href=\"//www.slideshare.net/weclouddata/deep-learning-introduction-weclouddata\" title=\"Deep Learning Introduction - WeCloudData\" target=\"_blank\">Deep Learning Introduction - WeCloudData</a> </strong> from <strong><a href=\"https://www.slideshare.net/weclouddata\" target=\"_blank\">WeCloudData</a></strong> </div> decorative learning path image left decorative learning path image right

Schedule

Instructors

Nidhi Arora

Instructor

Ms. Nidhi Arora is currently a data scientist at Intact. She has a Master's degree in Computer Software Engineering. Before joining Intact, she worked as a Data Scientist for Morgan Stanley where she introduced the art of capacity management by achieving around 95% accurate results for a Real-time Volume Predictor using GARCH, KNN, LSTM, and seasonal ARIMA. She has a passion for teaching and is currently working with WeCloudData as a part-time deep learning instructor.

Shaohua Zhang

Chief Instructor (WeCloudData) | Head of Data Science (BeamData) | Startup Advisor and Data Coach

Instructor

Shaohua is the Co-founder and Chief Instructor at WeCloudData. In the past few years, he has trained hundreds of students and helped many of them launch their data science careers. He is also the CEO and Head of Data Science at Beam Data where he works closely with the industry partners on implementing data science projects. Prior to co-founding the Toronto Institute of Data Science and Technology (WeCloudData), he led the data science team at BlackBerry and helped build the data science practice at Kik. He has also worked with big companies such as TD, Canadian Tire, Sunlife, CIBC, Communitech, and MaRS on upskilling their data teams.

Rhys Williams

Instructor

Rhys is an AI and Robotics lover and freelance developer. He's been teaching the Python for Data Science courses with WeCloudData and currently is in charge of the Deep Learning Capstone project course. He loves robotics, raspberry pi, deep learning, and has developed his AI robot Mr-B. Check out Mr-B's videos on his Instagram: https://www.instagram.com/rhysdgw/

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What students are saying

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The lectures have been amazing! Both instructors are awesome - it's obvious that they are experts in this domain. They both have the ability to explain concepts in such a way that they can be understood. The slides are also very helpful and provide a solid reference point for the topics discussed thus far. Overall, very happy that I am taking this course and would definitely recommend to colleagues/friends

Grace

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Thank you so much for coordinating an awesome course. The assistant instructor was really great in exposing to us how course material is applied in production-level environment. I also like the instructor's approach of pushing us to build from fundamental to real-project using Pytorch first. And then progressing to Tensorflow. Personally, I think it's a super awesome course, but I believe you really have to dig yourself into the contents and dedicate many head-banging hours. But course material is very practical both in theory and application. Thanks much

Jason Lee

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