Deep Learning

Building state-of-the-art models

What is Deep Learning?

Deep Learning, a subset of machine learning, has completely revolutionized the way we work with images (including videos, CT scans, MRIs, etc.) and sequential (including text, time-series, and audio) data. Self-driving cars, automatic cancer detection and computational photography are all examples of Deep Learning. AI systems can now write blogs, create art and music, talk like humans and much more. Fun fact, they all use deep learning under the hood.

📚 Prerequisites
Python, Data Science and Machine Learning
⏳ Duration
60 – 65 hrs
What you will learn in the course?

The course is mainly divided into 3 parts: basic Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks. In the first part, we will learn all the basic stuff like linear layers, activations, loss function, gradient descent, etc. that are important for build neural networks. We will also see how versatile neural networks are. In the second part, we will learn all about working with images. We will discuss some amazing applications of CNNs like GANs and AutoEncoders. In the third part, we will learn about RNNs that are very widely used for working with textual data. Finally, we will see some applications like Image Captioning where we combine both images and text in one architecture.

  • Introduction
  • Neural Networks basics
  • Classification
    • Binary Classification
    • Multi-class Classification
  • Regression
  • Convolutional Neural Networks
    • Introduction
    • Features Extraction
    • Transfer Learning
    • Famous Architectures
  • Style transfer
  • Siamese Net
  • Object Detection
  • AutoEncoders (for dimensionality reduction)
  • GANs (for generating images)
  • Collaborative Filtering
  • Tabular Modelling
  • Recurrent Neural Networks
    • Introduction
    • Language models
    • Text Classification
    • Sequence Labelling
  • Sequence to Sequence
    • NER and POS tagging
    • Neural Machine Translation
    • Image Captioning

By the course end . . .

You will have much better understanding of different types of Neural Networks and their working. Throughout the course, we will be using pytorch, because it’s very intuitive and transparent, which helps in understanding Neural Networks better. We will be reading lots of research papers and implementing them in the course. You will feel very comfortable in reading any new research paper.At the end you will only say we studied in best institute in Pune.

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