(1) Standard neural networks: real estate, online advertising

(2) Convolutional neural networks: applied in image recognition including photo tagging

(3) Recurrent Neural Networks: applied in sequence data including speech recognition, machine translation

(4) custom/hybrid neural networks: complex applications such as autonomous driving

application of neural networks in supervised learning

http://datahacker.rs/neural-networks-representation/

deep-learning representation

Because we have access to large columns of data, more computational power, and advanced algorithms.

For big data, large neural networks perform better than traditional ML models (SVM, logistic regression...)

Why is deep learning taking off?

(1) initialize parameters
(2) Implement the forward propagation to get the prediction
(3) compute the loss
(4) Implement the backward propagation to compute the derivatives of the parameters
(5) update the parameters
(6) repeat steps (2) to (5) to reduce the cost.

The learning circle of the neural network

- Start off trying to have one neural network do multiple things (can actually lead to better performance)
- differences and similarities amongst tasks is utilized to output a better result

Multi-task Learning

Benchmarking is a way to analyze how well your neural network is performing on a given standard set.
-Ensembling
-Multicrop at test time
-using open source code


Benchmarking

• Single neuron == linear regression without applying activation(perceptron)

• Basically a single neuron will calculate weighted sum of input(W.T*X) and then we can set a threshold to predict output in a perceptron. If weighted sum of input cross the threshold, perceptron fires and if not then perceptron doesn’t predict.

What is a Neural Network(NN)?

The working memory is the system that enables the ability to explicitly hold and manipulate pieces of information that are used to achieve a goal. Unlike humans, neural networks do not have a working memory.

Working Memory

Beyond standard categorical and numeric variables, deep learning practitioners frequently work with myriad unstructured data formats. These complex data types include text strings for natural language processing, images and point clouds for computer vision, and audio data. Handling these diverse formats requires specialized approaches and advanced tools compared to traditional tabular data.

Deep Learning Data Types

https://www.coursera.org/learn/neural-networks-deep-learning?specialization=deep-learning

University of Michigan - Ann Arbor

 Coursera offers a five course specialization that teaches the foundations of Deep Learning in terms of understanding "how to build neural networks" and learning "how to lead successful machine learning projects". The specialization consists of the following courses:
- Neural Networks and Deep Learning
- Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
- Structuring Machine Learning Projects
- Convolutional Neural Networks
- Sequence Models


https://www.coursera.org/specializations/deep-learning

Deep Learning Specialization

Neural Networks and Deep Learning

https://www.coursera.org/learn/deep-neural-network?specialization=deep-learning

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

https://www.coursera.org/learn/machine-learning-projects?specialization=deep-learning

Structuring Machine Learning Projects

https://www.coursera.org/learn/convolutional-neural-networks?specialization=deep-learning

Convolutional Neural Networks

https://www.coursera.org/learn/nlp-sequence-models

Sequence Models

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

Machine Learning Yearning (Deeplearning.ai)

https://www.coursera.org/professional-certificates/ibm-data-science
Beginner course

IBM Data Science Professional Certificate

https://www.coursera.org/specializations/jhu-data-science
ten course specialization on coursera offered by JHU staff

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