Why do we need it. How does it work. Where is it used.
4
4
What are they. Why do we need them. Where are they used.
3
What it's all about. Why is it important. How it's used in the real world.
2
5
List of tasks that appear in an ML practitioner's day-to-day. Insights on how to build products that perform those tasks.
1
What is it. Why do we need it. List of tools available.
2
What is it. Why do we need to measure it. Metrics that can used.
2
Why do we need it. What is it. Types of activation functions.
1
Why look at bias and variance. Errors resulting from them. Bias vs variance tradeoff.
1
What exactly is it. Why is it relevant. Where is it used.
1
Why do we need it. How it works. Use cases in the real world.
1
Why do we need it. How does it work. Where is it used.
1
2
Why do we need it. How does it work. Where is it used.