Deep CNN - ResNets 1. Deep Convolutional Neural Networks In recent years, neural networks have become much deeper, with state-of-the-art networks evolving from having just a few layers (e.g., AlexNet) to over a hundred layers. The main benefit of a very deep network is that it can represent very complex functions. It can also learn features at many different levels of abstraction, from edges (at the shallower layers, closer to the input) to very complex features (at the deeper layers, closer to the output).

UNets https://pyimagesearch.com/2022/02/21/u-net-image-segmentation-in-keras/

https://www.youtube.com/watch?v=3wdqO_vYMpA We will create a class where you can put in a youtube video that you want to detect objects in.

ISLR(10.3) Convolutional Neural Networks CNNs mimic to some degree how humans classify images, by recognizing specific features or patterns anywhere in the image that distinguish each particular object class. the network first identifies low-level features in the input image such as small edges, patches of color.. these low-level features are then combined to form higher-level features such as ears, eyes.. finally, the presence or absence of these higher-level features contributes to the probability of any given output class A CNN build up this hierarchy by combining two specialized types of hidden layers

Two Classification Examples: Create a mood classifer using the TF Keras Sequential API (binary) Build a ConvNet to identify sign language digits using the TF Keras Functional API (multiclass) 0. Load Packages 1 2 3 4 5 6 7 8 9 10 11 12 13 14 import math import numpy as np import h5py import matplotlib.pyplot as plt import scipy from PIL import Image from scipy import ndimage import tensorflow as tf from tensorflow.

Goal：build and train a basic GAN that can generate hand-written images of digits (0-9) by using Pytorch Build the generator and discriminator components of a GAN from scratch. Create generator and discriminator loss functions. Train the GAN and visualize the generated images. 0. Import Packages and visualizer function 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 import torch from torch import nn from tqdm.