Vanishing gradient explained in detail _ Day 20

First let’s explain what’s Vanishing Gradient Problem in Neural Networks Understanding and Addressing the Vanishing Gradient Problem in Deep Learning Understanding and Addressing the Vanishing Gradient Problem in Deep Learning Part 1: What is the Vanishing Gradient Problem and How to Solve It? In the world of deep learning, as models grow deeper and more complex, they bring with them a unique set of challenges. One such challenge is the vanishing gradient problem—a critical issue that can prevent a neural network from learning effectively. In this first part of our discussion, we’ll explore what the vanishing gradient problem is, how to recognize it in your models, and the best strategies to address it. What is the Vanishing Gradient Problem? The vanishing gradient problem occurs during the training of deep neural networks, particularly in models with many layers. When backpropagating errors through the network to update weights, the gradients of the loss function with respect to the weights can become exceedingly small. As a result, the updates to the weights become negligible, especially in the earlier layers of the network. This makes it difficult, if not impossible, for the network to learn the underlying patterns in the data. Why does this...

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34

Mastering Hyperparameter Tuning & Neural Network Architectures: Exploring Bayesian Optimization_ Day 19

In conclusion, Bayesian optimization does not change the internal structure of the model—things like the number of layers, the activation functions, or the gradients. Instead, it focuses on external hyperparameters. These are settings that control how the model behaves during training and how it processes the data, but they are not part of the model’s architecture itself. For instance, in this code, Bayesian optimization adjusts: So, while the model’s internal structure—like layers and activations—remains unchanged, Bayesian optimization helps you choose the best external hyperparameters. This results in a better-performing model without needing to re-architect or directly modify the model’s components....

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TensorFlow: Using TensorBoard, Callbacks, and Model Saving in Keras _. day 16

Mastering TensorFlow: Using TensorBoard, Callbacks, and Model Saving in Keras Mastering TensorFlow: Using TensorBoard, Callbacks, and Model Saving in Keras TensorFlow and Keras provide powerful tools for building, training, and evaluating deep learning models. In this blog post, we will explore three essential techniques: Using TensorBoard for visualization Utilizing callbacks to enhance model training Saving and restoring models Using TensorBoard for Visualization TensorBoard is an interactive visualization tool that helps you understand your model’s training dynamics. It allows you to view learning curves, compare metrics between multiple runs, and analyze training statistics. Installation !pip install -q -U tensorflow tensorboard-plugin-profile Setting Up Logging Directory We need a directory to save our logs. This directory will contain event files that TensorBoard reads to visualize the training process. from pathlib import Path from time import strftime def get_run_logdir(root_logdir="my_logs"): return Path(root_logdir) / strftime("run_%Y_%m_%d_%H_%M_%S") run_logdir = get_run_logdir() Saving and Restoring a Model Keras allows you to save the entire model (architecture, weights, and training configuration) to a single file or a folder. Saving a Model model.save("my_keras_model", save_format="tf") Loading a Model model = tf.keras.models.load_model("my_keras_model") Saving Weights Only model.save_weights("my_weights.h5") model.load_weights("my_weights.h5") Using Callbacks Callbacks in Keras allow you to perform actions at various stages of training (e.g., saving...

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15

Activation Function, Hidden Layer and non linearity. _ day 12

Understanding Non-Linearity in Neural Networks Understanding Non-Linearity in Neural Networks Non-linearity in neural networks is essential for solving complex tasks where the data is not linearly separable. This blog post explains why hidden layers and non-linear activation functions are necessary, using the XOR problem as an example. What is Non-Linearity? Non-linearity in neural networks allows the model to learn and represent more complex patterns. In the context of decision boundaries, a non-linear decision boundary can bend and curve, enabling the separation of classes that are not linearly separable. Role of Activation Functions The primary role of an activation function is to introduce non-linearity into the neural network. Without non-linear activation functions, even networks with multiple layers would behave like a single-layer network, unable to learn complex patterns. Common non-linear activation functions include sigmoid, tanh, and ReLU. Role of Hidden Layers Hidden layers provide the network with additional capacity to learn complex patterns by applying a series of transformations to the input data. However, if these transformations are linear, the network will still be limited to linear decision boundaries. The combination of hidden layers and non-linear activation functions enables the network to learn non-linear relationships and form non-linear decision boundaries. Mathematical...

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63

Activation Function _ day 11

Activation Functions in Neural Networks Activation Functions in Neural Networks: Why They Matter ? Activation functions are pivotal in neural networks, transforming the input of each neuron to its output signal, thus determining the neuron’s activation level. This process allows neural networks to handle tasks such as image recognition and language processing effectively. The Role of Different Activation Functions Neural networks employ distinct activation functions in their inner and outer layers, customized to the specific requirements of the network: Inner Layers: Functions like ReLU (Rectified Linear Unit) introduce necessary non-linearity, allowing the network to learn complex patterns in the data. Without these functions, neural networks would not be able to model anything beyond simple linear relationships. Outer Layers: Depending on the task, different functions are used. For example, a softmax function is used for multiclass classification to convert the logits to probabilities that sum to one, which are essential for classification tasks. Practical Application Understanding the distinction and application of different activation functions is crucial for designing networks that perform efficiently across various tasks. Neural Network Configuration Example Building a Neural Network for Image Classification This example demonstrates setting up a neural network in Python using TensorFlow/Keras, designed to classify...

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13

Regression vs Classification Multi Layer Perceptrons (MLPs) _ day 10

Regression with Multi-Layer Perceptrons (MLPs) Introduction Neural networks, particularly Multi-Layer Perceptrons (MLPs), are essential tools in machine learning for solving both regression and classification problems. This guide will provide a detailed explanation of MLPs, covering their structure, activation functions, and implementation using Scikit-Learn. Regression vs. Classification: Key Differences Regression Objective: Predict continuous values. Output: Single or multiple continuous values. Example: Predicting house prices, stock prices, or temperature. Classification Objective: Predict discrete class labels. Output: Class probabilities or specific class labels. Example: Classifying emails as spam or not spam, recognizing handwritten digits, or identifying types of animals in images. Regression with MLPs MLPs can be utilized for regression tasks, predicting continuous outcomes. Let’s walk through the implementation using the California housing dataset. Activation Functions in Regression MLPs In regression tasks, MLPs typically use non-linear activation functions like ReLU in the hidden layers to capture complex patterns in the data. The output layer may use a linear activation function to predict continuous values. Fetching and Preparing the Data from sklearn.datasets import fetch_california_housing from sklearn.model_selection import train_test_split # Load the California housing dataset housing = fetch_california_housing() # Split the data into training, validation, and test sets X_train_full, X_test, y_train_full, y_test = train_test_split(housing.data, housing.target,...

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38

What is Gradient Decent in Machine Learning? _ Day 7

Mastering Gradient Descent in Machine Learning Mastering Gradient Descent: A Comprehensive Guide to Optimizing Machine Learning Models Gradient Descent is a foundational optimization algorithm used in machine learning to minimize a model’s cost function, typically Mean Squared Error (MSE) in linear regression. By iteratively adjusting the model’s parameters (weights), Gradient Descent seeks to find the optimal values that reduce the prediction error. What is Gradient Descent? Gradient Descent works by calculating the gradient (slope) of the cost function with respect to each parameter and moving in the direction opposite to the gradient. This process is repeated until the algorithm converges to a minimum point, ideally the global minimum, where the cost function is minimized. Types of Learning Rates in Gradient Descent: Too Small Learning Rate Slow Convergence: A very small learning rate makes the algorithm take tiny steps toward the minimum, resulting in a long training process. High Precision: Useful when fine adjustments are needed to avoid overshooting the minimum, but impractical for large-scale problems due to time inefficiency. Too Large Learning Rate Risk of Divergence: A large learning rate can cause the algorithm to overshoot the minimum, leading to oscillations or divergence where the cost function increases instead of...

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26

Can we make prediction without need of going through iteration ? yes with the Normal Equation _ Day 6

Understanding Linear Regression: The Normal Equation and Matrix Multiplications Explained Understanding Linear Regression: The Normal Equation and Matrix Multiplications Explained Linear regression is a fundamental concept in machine learning and statistics, used to predict a target variable based on one or more input features. While gradient descent is a popular method for finding the best-fitting line, the normal equation offers a direct, analytical approach that doesn’t require iterations. This blog post will walk you through the normal equation step-by-step, explaining why and how it works, and why using matrices simplifies the process. Table of Contents Introduction to Linear Regression Gradient Descent vs. Normal Equation Step-by-Step Explanation of the Normal Equation Step 1: Add Column of Ones Step 2: Transpose of X (XT) Step 3: Matrix Multiplication (XTX) Step 4: Matrix Multiplication (XTy) Step 5: Inverse of XTX ((XTX)-1) Step 6: Final Multiplication to Get θ Why the Normal Equation Works Without Gradient Descent Advantages of Using Matrices Conclusion Introduction to Linear Regression Linear regression aims to fit a line to a dataset, predicting a target variable $y$ based on input features $x$. The model is defined as: $$ y = \theta_0 + \theta_1 x $$ For multiple features, it generalizes...

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23

Regression & Classification with MNIST. _ day 4

  A Comprehensive Guide to Machine Learning: Regression and Classification with the MNIST Dataset Introduction to Supervised Learning: Regression and Classification In the realm of machine learning, supervised learning involves training a model on a labeled dataset, which means the dataset includes both input data and the corresponding output labels. Supervised learning tasks can be broadly categorized into two types: regression and classification.     Regression tasks aim to predict continuous numerical values. For example, predicting house prices based on various features such as location, size, and number of bedrooms. The output is a continuous value that can range over an infinite set of possible values. Common regression algorithms include linear regression, decision trees, and support vector regression.     Classification, on the other hand, deals with predicting discrete categorical values. The goal is to assign input data to one of several predefined classes. For instance, classifying emails as either spam or not spam, or recognizing handwritten digits as one of the digits from 0 to 9. The output is a discrete value representing the class label. Popular classification algorithms include logistic regression, support vector machines, decision trees, and neural networks. The MNIST Dataset: A Benchmark for Classification The MNIST...

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88

Models based, Instance Models, Train-Test Splits: The Building Blocks of Machine Learning Explained – Day 3

In machine learning and deep learning, the concepts of Model vs Instance Models and Train-Test Split are closely intertwined. A model serves as the blueprint for learning patterns from data, while an instance model represents the specific realization of that blueprint after training. The train-test split, on the other hand, plays a critical role in the creation and evaluation of these instance models by dividing the dataset into subsets for training and testing. This blog post will delve into the relationship between these concepts,   first we explain model vs instance based and then we explain train- test spilt and provide two great examples to understand all we have explained better. These basics is mandatory to understand machine learning better:    Understanding Model-Based & Instance-Based Learning in Machine Learning Machine learning is a transformative technology that relies on various methods to teach computers how to learn from data and make predictions. Two fundamental approaches in this domain are model-based learning and instance-based learning. This blog post delves into these two learning paradigms, their differences, and how they relate to common issues like overfitting and underfitting. We will also explore how deep learning fits into this framework. Model-Based Learning Definition: Model-based...

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