This course on Deep Learning aims to provide learners with an introduction to deep learning and computer vision, building models from convolutions, programming in TensorFlow and Keras, transfer learning, data augmentation, and a deeper understanding of deep learning. The course covers topics such as stride length, dropout for better deep learning models, TPUs, systolic arrays, and bfloat16 to accelerate deep learning. The teaching method includes hands-on programming exercises and walkthroughs. This course is intended for individuals interested in expanding their knowledge of deep learning and computer vision techniques.
Overview
Syllabus
Intro to Deep Learning and Computer Vision | Kaggle.
Building Models from Convolutions | Kaggle.
Programming in TensorFlow and Keras | Kaggle.
Transfer Learning | Kaggle.
Data Augmentation | Kaggle.
A Deeper Understanding of Deep Learning | Kaggle.
Deep Learning from Scratch | Kaggle.
Stride Length and Dropout for Better Deep Learning Models | Kaggle.
TPUs, systolic arrays, and bfloat16: accelerate your deep learning | Kaggle.
Learn With Me: Getting Started with Tensor Processing Units (TPUs) | Kaggle.
TPUs: AlphaGo to Speech Recognition | Kaggle.
Getting Curious: What it takes to build a TPU | Kaggle.
TPU Notebook Walkthrough: Introduction to TFRecords | Kaggle.
Taught by
Kaggle
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Reviews
5.0 rating, based on 1 Class Central review
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Mukilesh KThis is the first time taking a class with Kaggle (Class Central), so the experience was unique to me. I wasn't quite sure what to expect, but I am very pleased with this format. The instructor was very knowledgable, worked at a good pace, and most importantly, was very good at communicating with all the attendees and ensuring questions were answered. I would definitely recommend Kaggle courses to others and I hope to have the opportunity to attend more training courses.
