rish-16

import qiskit as qk

# Load saved account from memory
qk.IBMQ.load_accounts()

n = 3
q = qk.QuantumRegister(n)
c = qk.ClassicalRegister(n)
circ = qk.QuantumCircuit(q, c)

rish-16

MA2108 Note 1

Definitions

1. A set is an unordered collection of unique elements. Examples are natural numbers, integers, rational numbers, and real numbers
2. Cartesian product of $A$ and $B$ is $A \times B = \lbrace(a, b) : a \in A, b \in B\rbrace$
3. A function from set $A$ to set $B$ is a rule of correspondence that assigns each element $x \in A$ to uniquely determined element $f(x) \in B$
4. Set $A$ is called the domain of $f$
5. Set $B$ is the codomain of $f$
6. The set $f(A) = \lbrace(x) : x \in A\rbrace$ is called the range of $f$
7. Althought $D(f) = A$, we only have $R(f) \subseteq B$

rish-16

Essay Writing

1. Effective Thesis
2. Parenthetical Citation
3. Essay Structure
4. Editing Tips
5. Peer Editing

Effective Thesis

• Tells a reader how you will interpret the significance of the subject matter under discussion. Such a statement is a debatable, overarching claim organising an essay

rish-16

import torch
import torch.nn as nn
import torch.nn.functional as F

"""
Creating tensors
"""
a = torch.rand(...) # returns a torch.Tensor
b = torch.LongTensor(10).random_(0, 2) # 10-dim vector from [0, 1]

rish-16

import os
import numpy as np
import random
import tensorflow as tf
from tfdeterminism import patch

def seed(s=42):
  random.seed(s)
  np.random.seed(s)
  tf.random.set_seed(s)

rish-16

PATH = "./my_dataset/" # path to dataset on Colab instance
TRAIN_PATH = PATH + "train/"
VAL_PATH = PATH + "val/"

# your custom augmentations
T = transforms.Compose([
      transforms.ToTensor(),
      ...
    ])

rish-16

device = xm.xla_device()

# define some hyper-params you'd feed into your model
in_channels = ...
random_param = ...

# create model using appropriate hyper-params
net = MyCustomNet(...)

# seat it atop the TPU worker device and switch it to train mode

rish-16

'''
num_replicas is the total number of times we'll replicate 
the batch samples for all cores.
'''
train_sampler = torch.utils.data.distributed.DistributedSampler(
                                                  im_train,
                                                  num_replicas=xm.xrt_world_size(),
                                                  rank=xm.get_ordinal(), 
                                                  shuffle=True
                 )

rish-16

!wget http://www.some_dataset_website.com/my_dataset.tar.gz
!tar -xvzf my_dataset.tar.gz

rish-16

import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms

import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from patchify import patchify, unpatchify # pip install patchify