90 docs tagged with "Machine Learning"

Introduction to nn.relu(), reduce_mean(), GradientTape(), one_hot(), and tf.data.Dataset

Basic machine learning implementation methods using the Scikit-Learn package

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

The Role of Bias in Machine Learning and Its Difference from Weights

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

The process of AI models learning from data, making predictions, and evaluating performance

How weights and biases are represented as matrices and stored in files

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

Understanding the concept of the cost function in machine learning and its difference from the loss function

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

Create a machine learning model to classify emails as spam using simple text data.

An introduction to categorical data and methods to convert it into numerical form

Problems with deeper neural networks and solutions to address them

Concepts and examples of Classification models that categorize data into specific groups in machine learning

Understanding the concept and application of K-Nearest Neighbors (KNN) in machine learning, which classifies based on proximity to surrounding data.

Understanding the concept and application of the coefficient of determination (R², R-squared) in evaluating the performance of machine learning regression models

A comparison of the differences and applications of Sigmoid, ReLU, and Softmax functions

Understanding the concept and importance of the Learning Rate in machine learning model training

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

Learn how to create a simple linear regression model using TensorFlow in Python

Hands-on exercise to develop an AI model using simple Python code

The role and significance of weights in machine learning

The concept and significance of batch size in influencing a machine learning model's training method

Understanding the concept and importance of an Epoch in the machine learning training process concerning how many times the data should be iterated

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

How to easily build deep learning models using Keras

Types and general structure of data contained in a dataset

Understanding and using the F1-Score to measure machine learning model performance

Understand the concept of Precision in evaluating machine learning model performance and how to utilize it

Understanding the concept of recall in measuring the performance of machine learning models

Understanding the concept and application of accuracy for evaluating the performance of machine learning models

Learn about the concept and mechanism of the Adam optimizer, which accelerates learning speed and stabilizes convergence in neural networks

The concept and importance of feature selection and dimensionality reduction

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

Understanding the concept and application of K-Means Clustering, an unsupervised machine learning algorithm for automatically grouping data

Methods and importance of handling missing values in data preprocessing

Comparing Key Differences Between Machine Learning and Deep Learning

How perceptrons take input values and make decisions by combining weights and biases.

Understanding the concept and importance of hyperparameters in optimizing the performance of machine learning models

How to perform simple image classification using CNN with code examples

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

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

Concepts and differences between label encoding and one-hot encoding

Understanding the concept and importance of training datasets used for machine learning model training

Explore how reinforcement learning, a method to train AI, works and its applications

Understand the concept and application of Logistic Regression for classifying data into distinct categories in machine learning.

The concept and role of Loss Function in machine learning

Understanding Optimization in Machine Learning and how Gradient Descent is used to adjust weights.

Understanding and applying Mean Absolute Error (MAE) for evaluating regression models in machine learning

Understanding the concept and application of Mean Squared Error (MSE) in evaluating the performance of machine learning regression models

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

The differences between normalization and standardization and the appropriate use cases for each

Understanding and applying the Softmax function used in multi-class classification in machine learning

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

Concepts and applications of Linear Regression for predicting numerical values in machine learning

An introduction to the concept and application of Random Forest, which predicts using multiple decision trees in machine learning

The concept and application methods of Decision Trees for classifying and predicting data in machine learning

The necessity of preprocessing and an example of JSONL data preprocessing

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

The concepts and differences between Classification and Regression, the problem types solved by machine learning models

Understanding regression models in machine learning for predicting continuous numerical values, with examples

Understanding the concept of data scaling and how to normalize using Min-Max Scaling

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

Standardization using mean and standard deviation to scale data

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

The methodology and limitations of supervised learning, a predominant AI training method

Understanding the concept and application of Support Vector Machine (SVM), a machine learning algorithm that finds the best boundary to separate data

Understanding Tensor Dimensions with Examples

Basics of Building AI Models with TensorFlow

The concept and significance of the test dataset used for evaluating the final performance of a machine learning model

The concept and role of labels in machine learning

The core concept for data representation in TensorFlow

Explore the definition, history, and applications of machine learning.

Understanding the concept and characteristics of the ReLU (Rectified Linear Unit) function used to activate neurons in machine learning.

How to train and evaluate neural network models in Keras

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.

Learn about the concept and application of the Sigmoid function in machine learning for converting numerical values to probabilities between 0 and 1.

Learn how to perform basic operations on tensors in TensorFlow with practical examples.

The concept and significance of a validation dataset in fine-tuning machine learning model performance

How unsupervised learning is conducted and its applications in AI training

Techniques to initialize weights in a neural network for effective learning

Understanding the concept of features in datasets and examples of their use in machine learning.

Key AI Types and Use Cases

Definition of AI Training and Its Similarity to Functions

The concept of Perceptron, its components, and activation functions

Understanding GPT, its background, and components

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

An easy explanation of why algorithms are necessary in machine learning and how they are applied