ML1---
Diabetes prediction with help of tensorflow sequential model. The first step in our journey is data acquisition. We load our dataset, a comprehensive collection of medical records meticulously gathered and anonymized for research purposes.
Once the dataset is loaded, we move on to data visualization. By visualizing the data, we can gain valuable insights and observe underlying patterns and trends. It allows us to understand the distribution of data, identify outliers, and even discover relationships between different health metrics.
After we have a good grasp of our data, we proceed to split it using the train_test_split() method. This step is vital for the evaluation of our model. The training set is used to train our model, while the test set is used to evaluate its performance. This ensures that our model is tested on unseen data, providing us with a realistic measure of its predictive power.
Next, we load the TensorFlow Sequential model. The Sequential model is a linear stack of layers, where each layer has exactly one input tensor and one output tensor.
With our model loaded and configured, we’re ready to train it. We feed our training data into the model, allowing it to learn from the patterns in the data. The model adjusts its internal parameters based on the data it sees, improving its predictive accuracy with each iteration.
Finally, after our model is trained, we use it to make predictions. We feed our test data into the model, and it outputs predictions for each data point. These predictions give us a probability of each patient having diabetes, based on their health metrics.
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ML2--- Embedding model
A machine learning model is employed to review wine quality. We have a vast dataset encompassing wine quality, taste, and region-specific descriptions. The dataset is categorized based on points to derive the target label, providing a comprehensive understanding of wine characteristics.
Two distinct embedding methods are utilized in our model. The first is the TensorFlow Hub Embedding Model, a pre-trained model that captures the nuances of wine descriptions and translates them into numerical vectors. This model is adept at handling high-dimensional data and extracting meaningful patterns.
The second method is the LSTM (Long Short-Term Memory) Embedding Model. This model excels in processing sequential data, capturing the temporal dependencies in the wine reviews. It’s particularly useful in understanding the context and sentiment of the reviews.
Together, these models provide a robust and comprehensive analysis of wine quality, offering valuable insights .
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ML3--- Machine Learning Algorithms
we have developed a machine learning model that leverages a dataset from a gamma telescope. This model is unique as it employs four different algorithms - Logistic Regression, Naive Bayes, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) - to predict outcomes based on the dataset.
Each algorithm brings its own strengths to the table. Logistic Regression provides a probabilistic approach, Naive Bayes offers simplicity with strong assumptions about the independence of features, KNN provides a non-parametric method that is good for complex decision boundaries, and SVM offers a powerful kernel trick for non-linear data.
The model is trained using each of these algorithms, and their performance is evaluated based on their accuracy. The algorithm with the highest accuracy is considered the most effective for this specific dataset.
This approach not only helps in finding the most accurate algorithm but also provides insights into how different machine learning algorithms perform under the same conditions. It’s a comprehensive method to harness the power of machine learning for gamma telescope data analysis.
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ML-4--- Detction of Disease using Machine Learning
Parkinson’s disease is a progressive disorder that affects the nervous system and the parts of the body controlled by the nerves. The symptoms are not easily noticeable and there are no definitive diagnostic methods available1.
The repository uses a dataset extracted from the UCI repository, which contains features from voice recordings of patients. These features are part of some other diagnostics which are generally used to capture the difference between a healthy and affected person.
The machine learning model in this repository is trained on this acoustic dataset of patients. The model uses Python libraries like Pandas, Numpy, Matplotlib, Sklearn, and Imblearn.
The goal of this project is to help in the detection of Parkinson’s disease, which is not easily diagnosed and detected. This shows the power of machine learning in solving real-world problems and potentially improving the lives of patients with Parkinson’s disease.
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ML-5--- Machine Learning for Drug Discovery
This is a bioinformatics project that uses machine learning for drug discovery. It utilizes the ChEMBL database, which is a manually curated database of bioactive molecules with drug-like properties. The project focuses on the coronavirus, and aims to identify potential drug candidates.
The Jupyter notebook in the repository uses Lipinski descriptors, RDKit, and PaDEL software for the analysis. Lipinski’s rule of five is a rule of thumb to evaluate druglikeness or determine if a chemical compound with a certain pharmacological or biological activity has properties that would make it a likely orally active drug in humans. RDKit is a collection of cheminformatics and machine learning tools. PaDEL software is used to calculate molecular descriptors and fingerprints.
The potential drug candidates are analyzed and in the end, a machine learning model is built using the Random Forest algorithm. Random Forest is a popular and versatile machine learning method that is capable of performing both regression and classification tasks. It also handles a large proportion of missing values and maintains accuracy when a large proportion of the data are missing.
The goal of the model is to check the efficiency of protein molecules for binding. This is crucial in drug discovery as the interaction between drug molecules and protein targets is the key to the therapeutic effect. The project demonstrates the power of machine learning in accelerating drug discovery and potentially contributing to the fight against diseases like coronavirus.
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ML-6--- Tunning Parameters and Hyperparameters in Machine Learning Model
This notebook you’re referring to is focused on building a neural network using TensorFlow. The project explores different parameters and hyperparameters for constructing the neural network.
The repository uses three different types of neural layers (16, 32, 64), two dropout layer probabilities (0, 0.2), and three different learning rates on three different batch sizes (32, 64, 128). Dropout is a regularization technique for reducing overfitting in neural networks by preventing complex co-adaptations on training data. Learning rate is a hyperparameter that determines how much we are adjusting the weights of our network with respect to the loss gradient.
The model is analyzed with the least validation loss to check accuracy. Validation loss is a metric that tells you how much error your model made on the validation dataset. It’s used to monitor the model during training and to choose the best version of the model.
In total, 54 different combinations of parameters and hyperparameters were analyzed. This comprehensive analysis helps in understanding the impact of different parameters and hyperparameters on the performance of the model.
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ML-7--- Introduction to Keras Tuner
The Keras Tuner is a library that helps you pick the optimal set of hyperparameters for your TensorFlow program. The process of selecting the right set of hyperparameters for your machine learning (ML) application is called hyperparameter tuning or hypertuning.
In this notebook, we will use the Keras Tuner to find the best hyperparameters for a machine learning model that classifies images of clothing from the Fashion MNIST dataset.
When you build a model for hypertuning, you also define the hyperparameter search space in addition to the model architecture. The model you set up for hypertuning is called a hypermodel.
In this Notebook, we use a model builder function to define the image classification model. The model builder function returns a compiled model and uses hyperparameters you define inline to hypertune the model.
Instantiate the tuner to perform the hypertuning. The Keras Tuner has four tuners available - RandomSearch, Hyperband, BayesianOptimization, and Sklearn. We use the Hyperband tuner.To instantiate the Hyperband tuner, you must specify the hypermodel, the objective to optimize and the maximum number of epochs to train (max_epochs).
The Hyperband tuning algorithm uses adaptive resource allocation and early-stopping to quickly converge on a high-performing model. This is done using a sports championship style bracket. The algorithm trains a large number of models for a few epochs and carries forward only the top-performing half of models to the next round. Hyperband determines the number of models to train in a bracket by computing 1 + logfactor(max_epochs) and rounding it up to the nearest integer.
The my_dir/intro_to_kt directory contains detailed logs and checkpoints for every trial (model configuration) run during the hyperparameter search. If you re-run the hyperparameter search, the Keras Tuner uses the existing state from these logs to resume the search. To disable this behavior, pass an additional overwrite=True argument while instantiating the tuner.
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ML-8 Image Generation using Stable Diffusion
This notebook uses Stable Diffusion, a method from Hugging Face Transformers, to generate high-quality images. It provides Python code for image generation based on textual prompts. The repository explores different parameters for image generation, and includes GPU acceleration for efficient processing.
It includes a warm-up model to run graph tracing before benchmarking. This process is crucial as it allows the model to reach a stable state, which can lead to more accurate benchmarking results.
The repository employs mixed precision and accelerated linear precision for image generation. Mixed precision training uses both 16-bit and 32-bit floating-point types in a model during training to make it run faster and use less memory. By using mixed precision, one can train models that are larger, train faster, or both.
The repository also provides a detailed explanation of how these processes work. Understanding these processes is crucial for anyone interested in machine learning and image generation. It helps users understand the underlying mechanisms of the model and how different parameters affect the output.
In summary, this Notebook is a comprehensive resource for anyone interested in image generation using Stable Diffusion. It provides detailed explanations and practical examples.
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ML-9 Linear Regression
The notebook you’re referring to is a comprehensive machine learning application that uses various algorithms for prediction. It includes Linear Regression, Multiple Linear Regression, and Neural Networks with both single and dense layers and at last calculate Mean square error.
Linear Regression is a basic predictive analytics technique. It is used when the outcome you are trying to predict is continuous. Multiple Linear Regression is an extension of simple linear regression used to predict an outcome variable based on several input predictor variables.
The repository also uses Neural Networks, which are a set of algorithms modeled loosely after the human brain, designed to recognize patterns. It includes both single layer and dense layers. Single layer neural networks can find patterns in linearly separable data. Dense layers are just regular layers of neurons in a neural network. Each neuron receives input from all the neurons in the previous layer, thus densely connected.
The repository uses these algorithms to predict the output of a loaded dataset. dataset - https://archive.ics.uci.edu/dataset/560/seoul+bike+sharing+demand
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