Neural networks are artificial systems inspired by biological neural networks that learn from data without being explicitly programmed. They work by analyzing datasets to identify characteristics and make predictions. For music recommendations, AI algorithms learn a user's music preferences from streaming history and other data, and then analyze songs to find similar music to suggest new songs and artists the user might like. Neural networks use layers like dense layers that perform operations to change vector dimensions and apply transformations. Weights, biases, and activation functions are important concepts that determine how neural networks process and output information.

1. Classification
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3. • We are talking about completely
personalized experiences, which are
already available today. Back in the
day, users had to physically shop
for vinyl records and cassettes.
• The introduction of digital music
allowed them to search for songs
online. However, the reason the AI
is changing the way we listen to
music is that we no longer have to
search for new songs. Instead,
algorithms and AI technology are
bringing new songs, albums, and
performers to us.

4. • The way this works is simple. AI
algorithms learn the users’ preference
in terms of genres, performers, and
albums. These systems are so
sophisticated that they not only
remember the user’s streaming history.
• They also analyze the chords, vocal
styles, and even the pitch of a song to
find similar playlists. Once the
algorithms analyze this data, they
suggest new songs, artists, and albums
that the user might like

5. • Besides improving the user
experience on streaming platforms,
artificial intelligence is making room
for advertisers in the industry.
• Personalized suggestions and
improved search engines can also
benefit new artists who want to
reach a wider audience and get
their music noticed.
• To provide these suggestions, AI
algorithms have to record large
amounts of data.
Specially
made for me

7. • Neural networks are artificial
systems that were inspired by
biological neural networks. These
systems learn to perform tasks by
being exposed to various datasets
and examples without any task-
specific rules.
• The idea is that the system
generates identifying
characteristics from the data they
have been passed without being
programmed with a pre-programmed
understanding of these datasets.

8. •Neural networks are
based on computational
models for threshold
logic. Threshold logic is a
combination of algorithms
and mathematics.
•Components of a typical
neural network involve
neurons, connections,
weights, biases,
propagation function, and
a learning rule.

10. • The dense layer is a neural network
layer that receives input from all
neurons of its previous layer. In the
background, the dense layer performs
a matrix-vector multiplication.
• The values used in the matrix are
actually parameters that can be
trained and updated with the help of
backpropagation.

11. • The output generated by the dense layer is an ‘m’ dimensional vector.
Thus, dense layer is basically used for changing the dimensions of
the vector.
• Dense layers also applies operations like rotation, scaling,
translation on the vector.

12. output = activation(dot(input, kernel) + bias)
In the above equation, activation is used for performing element-wise
activation and the kernel is the weights matrix created by the layer,
and bias is a bias vector created by the layer.

14. • Weights and biases (commonly referred to as w and b) are the
learnable parameters of a some machine learning models, including
neural networks.
• In an ANN, each neuron in a layer is connected to some or all of the
neurons in the next layer. When the inputs are transmitted
between neurons, the weights are applied to the inputs along with
the bias.

15. • Weights control the signal (or the strength of the connection)
between two neurons. In other words, a weight decides how much
influence the input will have on the output.
• Biases, which are constant, are an additional input into the next
layer that will always have the value of 1. Bias units are not
influenced by the previous layer but they do have outgoing
connections with their own weights.
• The bias unit guarantees that even when all the inputs are zeros
there will still be an activation in the neuron

16. • The activation function defines
the output of a neuron / node
given an input or set of input
(output of multiple neurons)
• They are the functions on which
the input is mapped to get the
output

17. Linear function

18. Binary function

19. Relu Function (REctified Linear Unit )

20. Sigmoid

21. Softmax

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