Untitled notebook

Mix.install([
  {:nx, "~> 0.9"},
  {:exla, "~> 0.9"},
  {:kino, "~> 0.14.2"},
  {:stb_image, "~> 0.6.9"},
  {:vega_lite, "~> 0.1.11"},
  {:kino_vega_lite, "~> 0.1.13"}
])

Speaking the Language of Data

Nx.default_backend(EXLA.Backend)

a = Nx.tensor([1, 2, 3])
b = Nx.tensor([4.0, 5.0, 6.0])
c = Nx.tensor([1,0,1], type: {:u, 8})

IO.inspect(a, label: :a)
IO.inspect(b, label: :b)
IO.inspect(c, label: :c)
nil

goog_current_price = 2677.32
goog_pe = 23.86
goog_mkt_cap = 1760

meta_current_price = 133.93
meta_pe = 11.10
meta_mkt_cap = 360

stocks_matrix =
  Nx.tensor([
    [goog_current_price, goog_pe, goog_mkt_cap],
    [meta_current_price, meta_pe, meta_mkt_cap]
    
  ])

IO.inspect(stocks_matrix)

nil

Scalar Multiplication

sales_day_1 = Nx.tensor([32, 10, 14])
sales_day_2 = Nx.tensor([10, 24, 21])
total_sales = Nx.add(sales_day_1, sales_day_2)

keep_rate = 0.9

unreturned_sales = Nx.multiply(keep_rate, total_sales)

price_per_product = Nx.tensor([9.95, 10.95, 5.99])
revenue_per_product = Nx.multiply(unreturned_sales, price_per_product)

Transpose

sales_matrix = Nx.tensor([
  [32, 10, 14],
  [10, 24, 21]
])

Nx.transpose(sales_matrix)

Linear Transformation

invert_color_channels =
  Nx.tensor([
    [-1, 0, 0],
    [0, -1, 0],
    [0, 0, -1]
  ])

"/Users/kantum/Downloads/IMG_1065.jpeg"
|> StbImage.read_file!()
# |> StbImage.resize(256,256)
|> StbImage.to_nx()
|> Nx.dot(invert_color_channels)
|> Nx.as_type({:u, 8})
|> Kino.Image.new()

vector = Nx.dot(Nx.tensor([1,2,3]), Nx.tensor([1,2,3]))
vector_matrix = Nx.dot(Nx.tensor([1,2]), Nx.tensor([[1],[2]]))
matrix_matrix = Nx.dot(Nx.tensor([[1, 2]]), Nx.tensor([[3],[4]]))

vector |> IO.inspect(label: :vector)
vector_matrix |> IO.inspect(label: :vector_matrix)
matrix_matrix |> IO.inspect(label: :matrix_matrix)
nil

Section