Deep Learning Checklist: 13 Months course

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Deep Learning is not hard. With correct approach, order and intuition, you can master it easily.
This checklist will guide you in the journey.


Week 1: Basics of DL

Make your foundation and intuition strong.

Week 2: Basic operations (ops)

DL is an architectural problem with many different types of bricks (operations).

Week 3: Concepts in Inference

Once you have a DL model, run it.

Week 4: Concepts in Training DL models

Make your model learn anything. This step held back DL for over 30 years.

Week 5: CNN Models

CNNs are the first widely successful DL models from Image/ Face Recognition to Object Detection in self-driving. The core concept is just a Convolution op.

Week 6: Other DL Architecture

Creating more architectures using the building block ops beyond CNN take the power of DL to solve all intellectual tasks.

Week 7: DL use cases

Understand the core tasks DL have mastered to solve and the architectures involved.

Week 8: TensorFlow/ PyTorch

DL is complex but implementing the DL ideas practically is relatively easy with the open-source frameworks.

Week 9: Optimization

Optimizing the training and inference process on various devices is a core focus area of DL for Engineers.

Week 10: Advanced Concepts

With a strong foundation, you can arrive at the advanced concepts on your own and contribute to the growth of DL.

Week 11: NLP Model

Deep Learning has mastered text based tasks as well with special architectures like BERT.

Week 12: LLMs

LLMs are the hottest topic today and DL has laid the foundation.

Week 13: DL Projects

Work on these unique DL projects and apply the experience to work on a new DL idea.

Best of Luck.

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