- When you are training on a set of tasks that could benefit from having shared lower-level features
- Usually: amount of data you have for each task is quite similar
- Can train big enough neural networks that are able  to do well on all tasks


When to use multi-task learning?

Multi-Task Learning

1) Hard parameter sharing
2) Soft parameter sharing


Two types of Multi task learning methods


- Samples sizes are increased as a general relationship to be found amongst all parameters is typically the goal
- Multi task learning helps focus data attention to relevant noise, ignoring less relevant data
- Multi task learning is a regularizer meaning it reduces overfitting


Why does Multi-task learning work?


- 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

University of Michigan - Ann Arbor

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

Neural Networks and Deep Learning

(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

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.

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