James Murdza jamesmurdza

00:00:02 hey everyone today we're going to be doing cnns or convolutional neural networks

doing cnns or convolutional neural networks and uh those are neural network models designed for processing structured grid data like images

00:00:06 cnns are neural networks that can be used to classify or detect images

networks that can be used to classify or detect images, and that's about all i know. passing it to naid. yeah, just like the simple.

00:00:24 the idea for cnns was inspired by biological networks, similar to simple neural networks

00:00:08 christopher nolan is a renowned movie director known for blockbuster hits like "the dark knight" and "inception"

the majority of blockbuster hits like the dark knight, inception, interstellar, and oppenheimer are from him. he's considered one of the best directors and one of my favorite founders. he runs a company called syncopy, which only has nine employees.

00:00:39 nolan's production company syncopy employs only nine full-time staff

nolan's movies, from oppenheimer to the rest, are produced by a company with only nine full-time employees. despite movies being massive productions, it's impressive he pulls off such large-scale projects with such a small team. pretty cool.

00:01:00 he hires hundreds or even thousands of people on set for short-term film projects

00:00:02 yo hey everyone here we are from beautiful san francisco

00:00:06 i'm here with my boy kareem, what's up kareem the dream

00:00:22 today, we're excited and looking forward to this live stream

00:00:32 kareem talks about being tired of the marvel vs dc debate

00:03:03 kareem wishes to create another comic book animation and gaming distribution company

00:00:18 welcome all right go ahead

00:00:25 today we are going to be talking about new networks

00:00:32 we'll discuss the motivation behind why researchers started looking into new networks

00:00:44 initially, we will cover why these networks have shown impressive performance now and not earlier

00:01:01 we will build on that and delve into the mathematical background

00:00:11 we just started one second ago.

00:00:21 today we are going to be talking about new networks.

00:00:27 I'll provide the motivation behind why researchers started looking into new networks.

00:00:44 we'll discuss why new networks have shown impressive performance just now, not earlier.

00:01:01 we'll build from understanding their mathematical background to discussing forward and backward pass.

	import torch
	import torch.nn as nn
	import torch.optim as optim

	# Define the model class
	class CustomModel(nn.Module):
	def __init__(self):
	super(CustomModel, self).__init__()
	# Define layers
	self.fc1 = nn.Linear(18, 10) # First hidden layer

	import tensorflow as tf

	# Define the model using TensorFlow's low-level API
	class CustomModel(tf.Module):
	def __init__(self):
	# Initialize weights for each layer
	self.dense1_weights = tf.Variable(tf.random.normal([18, 10]), name='dense1_weights')
	self.dense1_biases = tf.Variable(tf.zeros([10]), name='dense1_biases')

	self.dense2_weights = tf.Variable(tf.random.normal([10, 20]), name='dense2_weights')

	import React from 'react';

	import { getAvailability, formatDate, dateString, bookings, cancelBooking, registerBooking, accountInfo} from './api'

	class BookingModal extends React.Component {

	recieveData = (data) => {
	if (data) {
	var bookingsData = data["msg"];
	this.setState({

	import java.awt.*;
	import java.awt.event.*;
	import javax.swing.*;

	public class Main extends JPanel implements ActionListener, KeyListener {
	private Timer timer;
	private int playerX, playerY, playerVelocityY;
	private boolean isJumping, isMovingLeft, isMovingRight;
	private static final int PLAYER_WIDTH = 50;
	private static final int PLAYER_HEIGHT = 50;