Deep Learning

CNN intro CNN¶ A Convolutional Neural Network (CNN) is a type of Artificial Neural Network (ANN) specifically designed to process data that has a grid-like structure, such as images. CNNs are particularly good at recognizing patterns, shapes, and objects in images and have been highly successful in tasks like image …

CNN -Steps CNN Code Explanation¶Part 1: Building the CNN¶importing the keras Libraries and package¶from keras.models import Sequential from keras.layers import Convolution2D from keras.layers import MaxPooling2D from keras.layers import Flatten from keras.layers import Dense import tensorflow.python.framework.dtypes Here’s an explanation of why each of these components is used in building a Convolutional …

Convolution Neural Network – Itronix Solutions Download Dataset¶ https://www.kaggle.com/c/dogs-vs-cats/data Part 1: Train CNN Model¶ Step 1: Import the required packages¶ In [1]: from keras.models import Sequential from keras.layers import Conv2D from keras.layers import MaxPooling2D from keras.layers import Flatten from keras.layers import Dense Step 2: Initialising the CNN¶ In [2]: model = Sequential() …

Recurrent Neural Networks What are Recurrent Neural Networks?¶Recurrent Neural Network(RNN) are a type of Neural Network where the output from previous step are fed as input to the current step. In traditional neural networks, all the inputs and outputs are independent of each other, but in cases like when it …

RNN Steps To perform tasks using a Recurrent Neural Network (RNN), you can follow these steps: 1. Define the Problem¶ Identify the task: Determine the type of sequence data you want to model (e.g., time series prediction, language modeling, speech recognition). Collect and preprocess the data: Gather your dataset and …

RNN Intro RNN Intro¶ A Recurrent Neural Network (RNN) is a type of artificial neural network designed to handle data where the order of information is important, like sentences or time series. It has a “memory” that helps it remember past inputs, allowing it to make better predictions based on …

2 Plan of Attack What we will learn in this session ?¶ The Neuron The Activation Function How do Neural Network works ? How do Neural Networks Learn ? Gradient Descent Stochastic Gradient Descent Backpropagation 1 : THE NEURON¶A neuron (also called neurone or nerve cell) is a cell that …

3 – Practical In [1]: import sys print(sys.version) 3.12.5 (tags/v3.12.5:ff3bc82, Aug 6 2024, 20:45:27) [MSC v.1940 64 bit (AMD64)] pip install Theano!pip install Theano !pip install keras !pip install tensorflow In [2]: # Importing the libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd In [3]: # Importing the …

ai vs ml vs dl Introduction to Deep Learning¶Prerequisites of Deep Learning¶ Machine Learning Python or R AI vs ML vs DL¶ Types of Artificial Intelligence Difference Between Machine Learning and Deep Learning¶ Deep Learning is a machine learning technique thats learns features and task directly from the data, where …

Intro to ann An Artificial Neural Network (ANN) is a type of computer system designed to work similarly to how our brains process information. Just like our brains have neurons (nerve cells) that are connected and help us think and learn, ANNs use artificial neurons to process data and solve …