53 docs tagged with "Deep Learning"

Understanding the concept and role of strides when determining the movement interval of filters during convolution operations in CNN

The concept and role of Padding in maintaining or adjusting input size during convolution operations in CNNs

Understanding the concept of speech synthesis (TTS) and the process by which AI synthesizes speech

The concept of speech recognition and the process of how AI recognizes speech

Understanding the concept and functionality of the Convolution Operation for feature extraction in CNNs

The concept and operation of batch normalization to enhance training speed and stability in neural networks

Problems with deeper neural networks and solutions to address them

Understanding the concept and operation of Convolutional Neural Networks (CNN) for feature extraction in image data

Build a simple LSTM model to predict the next character using TensorFlow and Keras

The concept of deep learning and the structure of deep neural networks

An educational resource that explains what fine-tuning is and how it differs from general training in technical but easy-to-understand terms

Differences in the learning and application methods between GPT models and traditional machine learning models

Structural, training, and performance differences between GPT and Recurrent Neural Networks (RNN)

The concept and role of Filters in CNNs for detecting various patterns

The concept and mechanism of Forward Propagation, the process of computing outputs by passing input data through a neural network

Understanding the concept and application of a Fully Connected Layer in neural networks where each neuron is connected to every other neuron.

Definition and examples of a hidden layer

Comparing Key Differences Between Machine Learning and Deep Learning

Understanding the concept and mechanism of the Backpropagation algorithm, which adjusts weights to reduce prediction errors in neural networks

How to perform simple image classification using CNN with code examples

Explaining the impact of the number of Hidden Layers in neural networks on model performance

Definition and example of an input layer

Key components of neural networks, including Layers and Neurons

An overview of Machine Learning and Deep Learning concepts and their applications

Concepts and applications for analyzing input data and extracting features in machine learning and neural networks

Techniques to improve generalization performance of neural networks and machine learning models through L1 and L2 regularization

Understanding the concept and operation of Batch Gradient Descent in machine learning, where the entire dataset is used for weight adjustment

Understanding the limitations of a single-layer perceptron with the XOR problem and the role of multi-layer perceptrons

Understanding the concept and application of analyzing input data and extracting features in machine learning and neural networks

Explanation of the concept and operation of LSTM, which complements the limitations of RNNs by remembering long-distance information

The concept and operation of Recurrent Neural Networks (RNN) that handle data changing over time

Definition and examples of the output layer

The concept and role of pooling in CNN to reduce computational load while retaining essential features

The concept and functioning of the dropout technique in neural networks to prevent overfitting

Explore how the number of layers in a neural network affects its performance and learn how to set the optimal number of layers.

The concept and operation of GRU (Gated Recurrent Unit), which simplifies the complex structure of LSTM

Understand the concept and operation of Stochastic Gradient Descent (SGD) used for adjusting weights in machine learning

Internal structure of RNN and its method of processing information in time sequence

Understanding the concept and mechanics of gradient descent, which adjusts weights to minimize loss in neural networks

Understanding the role of activation functions in neural networks and why they are important

An overview of transfer learning and how it utilizes pre-trained models to solve new problems

Explanation of the concept and structure of Transformers, which process entire sentences simultaneously, as opposed to sequentially processing with RNNs.

Concepts, structure, and advantages of the Transformer model

The concept and mechanics of Multi-Head Attention, an extension of Self-Attention

Educational content explaining the concept of tokenization and its use in GPT.

Understanding the concept and mechanism of Momentum Optimization to enhance learning speed and stable convergence in neural networks

Techniques to initialize weights in a neural network for effective learning

Key AI Types and Use Cases

The concept and components of neural networks

Concept and Role of Neurons

Learn the concept and working mechanism of the perceptron, the basic unit of neural networks.

Key concepts and examples of deep learning, including neurons, layers, and learning

Understanding the concept and impact of the long-term dependency problem where RNNs struggle to remember past information for extended periods.