import numpy as np import matplotlib.pyplot as plt %matplotlib inline %config InlineBackend.figure_format = 'retina' import torch import torch.nn as nn import torch.nn.funct…
import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset import numpy as np import matplotlib.pyplot as plt
from ultralytics import YOLO, checks, hub import pandas as pd
import numpy as np import matplotlib.pyplot as plt %matplotlib inline %config InlineBackend.figure_format = 'retina'
import numpy as np import matplotlib.pyplot as plt %matplotlib inline import torch import torch.nn as nn import torch.nn.functional as F %config InlineBackend.figure_format =…
import torch import torch.nn as nn import matplotlib.pyplot as plt import numpy as np # Retina mode %config InlineBackend.figure_format = 'retina'
# Create training and…
import tiktoken
encoding = tiktoken.get_encoding("cl100k_base")
encoding.encode("Hello World! This is a simple notebook")
[9906, 4435, 0, 1115, 374, 264, 4382, 38266]
import numpy as np import time import matplotlib.pyplot as plt import pandas as pd # Retina display %config InlineBackend.figure_format = 'retina'
log_size = 8 size = 2…
import matplotlib.pyplot as plt import torch %matplotlib inline %config InlineBackend.figure_format='retina'
# Download some MNIST to demonstrate super-resolution from …
import networkx as nx import numpy as np import matplotlib.pyplot as plt import pandas as pd %matplotlib inline # Retina display %config InlineBackend.figure_format = 'ret…
import numpy as np import matplotlib.pyplot as plt import torch import seaborn as sns import pandas as pd dist =torch.distributions sns.reset_defaults() sns.set_context(…
import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import numpy as np import matplotlib.pyplot as plt from torch.utils.data imp…
from jax import vmap, jit, grad, vmap import jax.numpy as jnp # Enable 64-bit mode from jax.config import config config.update("jax_enable_x64", True) import matplotlib.pyp…
import jax.numpy as jnp import jax from jax import random import tensorflow_probability.substrates.jax as tfp tfd = tfp.distributions import pandas as pd import matplotlib.…
import numpy as np import matplotlib.pyplot as plt import torch import seaborn as sns from functools import partial sns.reset_defaults() sns.set_context(context="talk"…
import numpy as np import matplotlib.pyplot as plt import torch import seaborn as sns import pandas as pd import pyro dist =pyro.distributions sns.reset_defaults() sns.s…
import numpy as np import matplotlib.pyplot as plt import torch import seaborn as sns import pandas as pd t_dist =torch.distributions sns.reset_defaults() sns.set_contex…
import torch dist = torch.distributions import matplotlib.pyplot as plt import seaborn as sns import numpy as np %matplotlib inline
learn
import torch from jax import grad import jax.numpy as jnp
using Plots theme(:default) using LinearAlgebra using LaTeXStrings