Deep Learning is a new part of Machine Learning, which has been introduced with the objective of moving Machine Learning closer to Artificial Intelligence. Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. Through this training we are going to learn and apply concepts of deep learning with live projects.
The course includes the following;
•Prediction in Structured/Tabular Data
•Recommendation
•Image Classification
•Image Segmentation
•Object Detection
•Style Transfer
•Super Resolution
•Sentiment Analysis
•Text Generation
•Time Series (Sequence) Prediction
•Machine Translation
•Speech Recognition
•Question Answering
•Text Similarity
•Image Captioning
•Image Generation
•Image to Image Translation
We will be learning the followings:
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The theory and math underlying deep learning
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How to build artificial neural networks
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Architectures of feedforward and convolutional networks
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Building models in PyTorch
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The calculus and code of gradient descent
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Fine-tuning deep network models
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Learn Python from scratch (no prior coding experience necessary)
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How and why autoencoders work
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How to use transfer learning
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Improving model performance using regularization
