◼️ Perceptron
◾ Loss
- Recurrent Neural Networks
- Deep RNN
- LSTM
- GRU
- Oparetions on RNN
- Bidirectional Recurrent Neural Networks
- Sequence to sequence models
- Encoder–Decoder Network for Neural Machine Translation
- Neural Network
- Neuron
- Synapse
- Weights
- Bias
- Layers
- Weighted Input
- Activation Functions
- Loss Functions
- Optimization Algorithms
- Gradient Accumulation
- Co-occurrence Matrix
- Negative sampling
- Time series
What is AI ↗️
Artificial intelligence (AI) is the ability of a computer or a robot controlled by a computer to do tasks that are usually done by humans because they require human intelligence and discernment.
Deep learning implies the use of neural networks. The "deep" in deep learning refers to a neural network with many hidden layers.
Neural networks accept input and produce output. The input to a neural network is called the feature vector.
- The size of this vector is always a fixed length.
- Changing the size of the feature vector usually means recreating the entire neural network.
- Though the feature vector is called a "vector", this is not always the case. A vector implies a 1D array. In convolutional neural networks (CNNs), which can allow the input size to change without retraining the neural network.
Historically the input to a neural network was always 1D. However, with modern neural networks, you might see input data, such as:
- 1D Vector - Classic input to a neural network, similar to rows in a spreadsheet. Common in predictive modeling.
- 2D Matrix - Grayscale image input to a convolutional neural network (CNN).
- 3D Matrix - Color image input to a convolutional neural network (CNN).
- nD Matrix - Higher order input to a CNN.
The term dimension can be confusing in neural networks. In the sense of a 1D input vector, dimension refers to how many elements are in that 1D array.
- Example a neural network with 10 input neurons has 10 dimensions.
- However, now that we have CNN's, the input has dimensions too.
- The input to the neural network will usually have 1, 2 or 3 dimensions. 4 or more dimensions is unusual.
- You might have a 2D input to a neural network that has 64x64 pixels.
- This would result in 4,096 input neurons.
- This network is either** 2D or 4,096D, depending on which set of dimensions you are talking about!
There are usually four types of neurons in a neural network:
- Input Neurons - We map each input neuron to one element in the feature vector.
- Hidden Neurons - Hidden neurons allow the neural network to be abstract and process the input into the output.
- Output Neurons - Each output neuron calculates one part of the output.
- Bias Neurons - Work similar to the y-intercept of a linear equation.
These neurons are grouped into layers:
- Input Layer - The input layer accepts feature vectors from the dataset. Input layers usually have a bias neuron.
- Output Layer - The output from the neural network. The output layer does not have a bias neuron.
- Hidden Layers - Layers that occur between the input and output layers. Each hidden layer will usually have a bias neuron.
The input neurons accept data from the program for the network. The output neuron provides processed data from the network back to the program. The program will group these input and output neurons into separate layers called the input and output layers. The program normally represents the input to a neural network as an array or vector. The number of elements contained in the vector must equal the number of input neurons. For example, a neural network with three input neurons might accept the following input vector:
Neural networks typically accept floating-point vectors as their input. To be consistent, we will represent the output of a single output neuron network as a single-element vector. Likewise, neural networks will output a vector with a length equal to the number of output neurons. The output will often be a single value from a single output neuron.
Hidden Neurons
Hidden neurons have two essential characteristics.
- First, hidden neurons only receive input from other neurons, such as input or other hidden neurons.
- Second, hidden neurons only output to other neurons, such as output or other hidden neurons. Hidden neurons help the neural network understand the input and form the output.
Bias is disproportionate weight in favour of or against a thing or idea usually in a prejudicial, unfair, and close-minded way. In most cases, bias is considered a negative thing because it clouds your judgement and makes you take irrational decisions.
However, the role of bias in neural network and deep learning is much different.
Whenever you feed your neural network with data, it affects the model’s behaviour.
So, if you feed your neural network with biased data, you shouldn’t expect fair results from your algorithms. Using biased data can cause your system to give very flawed and unexpected results.
For example, consider the case of Tay, a chatbot launched by Microsoft. Tay was a simple chatbot for talking to people through tweets. It was supposed to learn through the content people post on Twitter. However, instead of being a simple and sweet chatbot, Tay turned into an aggressive and very offensive chatbot. People were spoiling it with numerous abusive posts which fed biased data to Tay and it only learned offensive phrasings. Tay was turned off very soon after that.
- Programmers add bias neurons to neural networks to help them learn patterns.
- Bias neurons function like an input neuron that always produces a value of 1.
- Because the bias neurons have a constant output of 1, they are not connected to the previous layer.
- The value of 1, called the bias activation, can be set to values other than 1. However, 1 is the most common bias activation.
- Not all neural networks have bias neurons.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition by Aurélien Géron
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