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What Are Neural Networks?
What Are Neural Networks? Table Of Contents: Neural Networks? What Are Neurons? What Are Layers? Weights & Biases. Activation Functions. Feedforward and Backpropagation. Deep Neural Networks. (1) Neural Networks? Neural networks, also known as artificial neural networks (ANNs), are computational models inspired by the structure and functioning of the human brain’s neural networks. They consist of interconnected artificial neurons, also called nodes or units, organized in layers. (2) What Are Neurons? Neurons are the basic building blocks of neural networks. Artificial neurons receive input signals, perform computations, and produce an output signal. Each neuron applies an activation function to the
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Introduction To Deep Learning.
Introduction To Deep Learning Table Of Contents: What Is Deep Learning? Applications Of Deep Learning. Importance Of Deep Learning. History Of Deep Learning. What Are Neural Networks? How Do Neural Networks Work? (1) What Is Deep Learning ? Deep learning is a subfield of machine learning and artificial intelligence (AI) that focuses on training artificial neural networks to learn and make intelligent decisions. It is characterized by the use of deep neural networks, which are neural networks with multiple layers of interconnected artificial neurons. In deep learning, the term “deep” refers to the depth of the neural networks, meaning they
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Deep Learning Syllabus
Deep Learning Syllabus (1) Introduction To Deep Learning: Overview of deep learning and its applications. Historical development and milestones in deep learning. Basics of neural networks and their components. (2) Artificial Neural Networks: Perceptrons and activation functions Feedforward neural networks Training algorithms: gradient descent, backpropagation Regularization techniques: dropout, weight decay Optimization algorithms: stochastic gradient descent, Adam Initialization strategies (3) Convolutional Neural Networks (CNNs): Introduction to CNNs and their architecture. Convolutional layers, pooling layers, and fully connected layers CNN training and optimization CNN applications in computer vision tasks (e.g., image classification, object detection) (4) Recurrent Neural Networks (RNNs): Introduction to RNNs
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Transformers Neural Networks
Transformers Neural Networks Table Of Contents: What Is Transformer? Key Features Of Transformer? Applications Of Transformer? Key Features Of Transformer? How Do Transformers Handle The Issue Of Vanishing Gradients In Long-Term Dependencies? (1) What Is Transformer? Transformers are a type of deep learning model architecture that have gained significant attention and popularity, particularly in natural language processing (NLP) tasks. Unlike traditional recurrent neural networks (RNNs), transformers rely on a self-attention mechanism to capture dependencies between different elements of the input sequence. (2) Key Features Of Transformer? Self-Attention Mechanism: The self-attention mechanism is the core component of transformers. It allows the
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Recurrent Neural Networks(RNN)
Recurrent Neural Networks Table Of Contents: What Is Recurrent Neural Networks? (1) What Is Recurrent Neural Networks? Recurrent Neural Networks (RNNs) are a type of artificial neural network designed to process sequential and time-dependent data. They are particularly effective in tasks involving sequential data, such as natural language processing, speech recognition, time series analysis, and handwriting recognition. The key feature of RNNs is their ability to maintain a hidden state that captures information from previous time steps and propagates it to future steps. This recurrent connectivity allows RNNs to capture temporal dependencies and patterns in the data. (2) Components Of
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Multi Horizon Forecasting
Multi Horizon Forecasting Table Of Contents: What Is Multi Horizon Forecasting? (1) What Is Multi Horizon Forecasting? Multi-horizon forecasting in time series refers to predicting future values of a time series over multiple future time steps. Instead of making a single-step forecast, where you predict the next value in the time series, multi-horizon forecasting involves predicting several future values at once, typically for a predefined range or sequence of future time steps. For example, in a daily sales forecasting scenario, a single-step forecast would predict tomorrow’s sales based on today’s data. In contrast, a multi-horizon forecast might predict the sales
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AR – Model Python Example
AR – Model Python Example Table Of Contents: Steps Involved In Time Series Forecasting. Python Example For Electricity Consumption Prediction. (1) Steps Involved In Time Series Forecasting. The following are some of the key steps which need to be done for training the AR model: Plot The Time-Series Check The Stationarity Determine The Parameter ‘p’ or Order Of The AR Model Train The Model Predict From The Model (2) Electricity Forecasting. Importing Required Libraries: import pandas as pd import numpy as np import matplotlib.pyplot as plt from statsmodels.tsa.ar_model import AutoReg Reading Input Data: df = pd.read_csv(‘Electric_Consumption.csv’) Plotting Consumption Details: df[‘Consumption’].plot(figsize=(20,
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Auto Regressive Time Series Model.
Auto Regressive Time Series Model. Table Of Contents: Auto Regression Model. Assumptions Of Auto Regression Model. AR(P) Model. AR(P) Model Examples. Examples Of AR Model. How Auto-Regressive Model Works? Auto Correlation Function (ACF). Interpreting ACF Plot. Partial AutoCorrelation Function(PACF). Interpreting PACF Plot. (1) Auto Regressive Model An autoregressive (AR) model is a type of time series model that predicts the future values of a variable based on its past values. It assumes that the current value of the variable depends linearly on its previous values and a stochastic (random) term. The term “autoregressive” refers to the fact that the model
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Time Series Decomposition.
Time Series Decomposition Technique. Table Of Contents: Introduction. Decomposition (1) Decomposition Decomposition in time series analysis refers to the process of breaking down a time series into its individual components, typically trend, seasonality, and remainder (or residual) components. The decomposition allows us to better understand the underlying patterns and variations present in the time series data. (2) Components Of Time Series Trend: The trend component represents the long-term direction or movement of the time series. It captures the overall systematic increase or decrease in the series over time. The trend can be linear, polynomial, exponential, or other functional forms depending
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Time Series Algorithms.
Time Series Algorithms Table Of Contents: Introduction. Decomposition AR MA ARIMA Seasonal ARIMA (SARIMA) Exponential Smoothing (ES) Vector Autoregression (VAR) Seasonal Decomposition of Time Series (STL) Gaussian Processes (GP) Long Short-Term Memory (LSTM) Networks Prophet Framework State-Space Models Bayesian Structural Time Series (BSTS) Holt-Winters Exponential Smoothing Box-Jenkins Multivariate Models Box-Jenkins ARIMA Models Intervention Analysis Wavelet Analysis Time Series Clustering (1) Introduction Time series analysis can offer valuable insights into stock prices, sales figures, customer behaviour, and other time-dependent variables. By leveraging these techniques, businesses can make informed decisions, optimize operations, and enhance long-term strategies. Time series analysis offers a multitude
