Deep learning
Neural networks from the ground up: layers, optimisers, CNNs, RNNs, GANs and training tricks.
Page 8 of 10
- Multi-Layer Perceptron (MLP) using Python11 min
A Multi-Layer Perceptron is a type of feedforward artificial neural network. It consists of at least three layers of nodes: an input layer, one or more...
- My Notes About Neural Network Design in a Comprehensive Way12 min
Neural Network Training: Loss Functions, Activation Functions, and Architecture Design
- Neural Network Forward Pass in Python7 min
A forward pass in a neural network is the process of propagating input data through the network to generate an output. This fundamental operation involves a...
- Neural Networks and Deep Learning Concepts8 min
Neural networks are the backbone of deep learning, inspired by the human brain's structure. They consist of interconnected nodes (neurons) organized in...
- Neural Networks as Universal Function Approximators8 min
Neural networks fundamentally operate as universal function approximators, capable of learning complex mappings between inputs and outputs through...
- Neural Networks as Universal Function Approximators in Python10 min
Neural networks are powerful models capable of approximating any continuous function to arbitrary precision. This property, known as universal approximation...
- Neural Networks Layered Learning for Pattern Recognition14 min
Neural networks are computational models inspired by the human brain, designed to recognize patterns and solve complex problems. They consist of...
- Perceptron Model and Implementation in Python6 min
The perceptron is a fundamental building block of neural networks, representing a mathematical model of a biological neuron. It takes multiple input signals...
- Simple Neural Network Implementation in Python9 min
Neural networks are computational models inspired by the human brain. They consist of interconnected nodes (neurons) that process and transmit information....
- Understanding Perceptrons A Python Implementation12 min
A perceptron is a simple artificial neuron that forms the building block of neural networks. It takes multiple inputs, applies weights to them, sums them up...
- Understanding the Role of Epochs in Training Neural Networks9 min
A fundamental concept in deep learning, epochs represent complete iterations through the training dataset. Each epoch allows the model to see every example...
- Walkthrough of Neural Network Training Process9 min
Constructing a neural network using objects and arrays to establish the foundational building blocks of deep learning. This implementation focuses on...
- Weight and Bias Fundamentals for Deep Learning11 min
Neural network weights and biases are critical parameters that determine how input signals are transformed through the network. Proper initialization of...
- Weight Initialization Techniques for Deep Learning in Python11 min
Weight initialization is a crucial step in training deep neural networks. It involves setting initial values for the network's parameters before the training...
- Weight Initialization Techniques in Deep Learning12 min
Deep learning models are highly sensitive to initial weight values. A proper initialization scheme helps prevent vanishing/exploding gradients and ensures...
- Weights and Activation Functions in Deep Learning7 min
Weights and activation functions are fundamental components of neural networks in deep learning. Weights determine the strength of connections between...
- Weights and Biases in Neural Networks in Python6 min
Neural networks are composed of interconnected nodes called neurons, and the connections between these neurons are represented by weights. Biases are...
- Weights in Machine Learning and Deep Learning with Python8 min
Weights are fundamental components in machine learning models, serving as the parameters that the model learns during training. They determine the strength...
- 15 Ways To Optimize Neural Network Training6 min
AdamW and Adam are popular optimization algorithms for training neural networks. These optimizers adapt learning rates for each parameter, leading to faster...
- Accelerating Deep Learning with Mixed Precision Training10 min
Mixed precision training is a technique that can significantly accelerate deep learning model training by using lower precision formats, such as float16...
- Addressing Saturation in Neural Network Training9 min
Saturation in neural networks occurs when the activation functions of neurons reach their maximum or minimum values, causing gradients to approach zero. This...
- Advanced Deep Learning Design Patterns and Optimization in Python8 min
Building a neural network from scratch provides deep understanding of backpropagation mechanics and gradient flow. This implementation demonstrates...
- Avoiding Overfitting in Neural Networks7 min
In neural networks, dropout is a regularization technique that prevents overfitting by randomly deactivating neurons during training with a specified...
- Batch Normalization in Neural Networks11 min
Batch Normalization is a technique used in neural networks to normalize the inputs of each layer. It helps improve training speed, stability, and...
- Batch Normalization in Neural Networks with Python10 min
Batch Normalization is a technique used to improve the training of deep neural networks by normalizing the inputs of each layer. It addresses the problem of...
- Batch Normalization Optimizing Deep Neural Network Training10 min
The mathematical foundation of Batch Normalization involves computing the mean and variance across a mini-batch, then normalizing and applying learnable...
- Batch Normalizations Impact on Learning Rate8 min
Batch Normalization is a technique used to improve the training of deep neural networks by normalizing the inputs of each layer. This process helps to reduce...
- Batch Processing Efficiency in Neural Networks12 min
Neural networks process data in batches to optimize computational efficiency and improve model convergence. Batch processing allows parallel computation of...
- Deep Learning Hyperparameter Tuning and Regularization in Python5 min
Here's a slideshow on Deep Learning Hyperparameter Tuning Regularization using Python:
- Distributed Training Strategies for Deep Learning6 min
Distributed training enables processing large-scale deep learning models across multiple GPU devices by parallelizing computations. This paradigm has become...