AI News, Neural Networks and Deep Learning

Neural Networks and Deep Learning

About this course: If you want to break into cutting-edge AI, this course will help you do so.

Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities.

Understand the key parameters in a neural network's architecture This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description.

Neural Networks and Deep Learning

About this course: If you want to break into cutting-edge AI, this course will help you do so.

Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities.

Understand the key parameters in a neural network's architecture This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description.

Thoughts after taking the Deeplearning.ai courses

DL practitioners and ML engineers typically spend most days working at an abstract Keras or TensorFlow level.

In classic Ng style, the course is delivered through a carefully chosen curriculum, neatly timed videos and precisely positioned information nuggets.

Andrew picks up from where his classic ML course left off and introduces the idea of neural networks using a single neuron(logistic regression) and slowly adding complexity — more neurons and layers.

By the end of the 4 weeks(course 1), a student is introduced to all the core ideas required to build a dense neural network such as cost/loss functions, learning iteratively using gradient descent and vectorized parallel python(numpy) implementations.

Andrew patiently explains the requisite math and programming concepts in a carefully planned order and a well regulated pace suitable for learners who could be rusty in math/coding.

If your math is rusty, there is no need to worry — Andrew explains all the required calculus and provides derivatives at every occasion so that you can focus on building the network and concentrate on implementing your ideas in code.

Then he slowly explains more details about how the car works — why rotating the wheel makes the car turn, why pressing the brake pedal makes you slow down and stop etc.

He keeps getting deeper into the inner workings of the car and by the end of the course, you know how the internal combustion engine works, how the fuel tank is designed etc.

Once you are comfortable creating deep neural networks, it makes sense to take this new deeplearning.ai course specialization which fills up any gaps in your understanding of the underlying details and concepts.

Andrew stresses on the engineering aspects of deep learning and provides plenty of practical tips to save time and money — the third course in the DL specialization felt incredibly useful for my role as an architect leading engineering teams.

Which Leading Artificial Intelligence Course Should You Take and What Should You Do After?

The DLND is broken into six parts with five of the parts having significant projects attached.

[NEW] Each project is focused on giving you hands-on experience with a certain type of deep learning technique.

Term 1 is focused on traditional AI methods and Term 2 is focused on deep learning.

Build a Game-Playing Agent — use adversarial search with heuristic evaluations to build a sudoku solving and isolation playing agent.2.

Implement a Planning Search — build an air cargo logistics system using planning graph heuristics.3.

I had completed the DLND upon signing up so I have access to all three.* The content of Term 1 was hard for me to ingest.

As I had already been learning about deep learning and other machine learning techniques, the topics covered in Term 1 difficult due to my lack of programming ability.

Most recently, I’ve been practicing implementing the steps of a Natural Language Processing pipeline (text preprocessing, feature extraction and modelling).

But what *is* a Neural Network? | Chapter 1, deep learning

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