Machine Learning illustration pack (40 illustrations)
♛ PRO 40 icons in this pack · included with Pro
- Every Pro pack, plus future ones
- Icon editor and recolour
- Molecules, Proteins, Gel Builder and Well Plate exports
- Use in papers, talks and commercial work
$49 one-time · lifetime (₹2,999 in India) · Get Pro
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Machine Learning is a Sciicons Pro illustration pack of 40 original, scientifically accurate illustrations. Machine learning and AI for papers, posters, slides and teaching: the perceptron, fully connected networks, activation functions (ReLU, sigmoid, tanh), backpropagation, loss curves, gradient descent and dropout; architectures (CNN, convolution kernel, LSTM cell, transformer block, self-attention heads, encoder–decoder, autoencoder, GAN, diffusion model); classic ML (decision tree, random forest, SVM, k-means, k-NN, linear and logistic regression, PCA); training and evaluation (train/validation/test split, k-fold cross-validation, overfitting and underfitting, confusion matrix, ROC and precision–recall curves, hyperparameter tuning); data and representation (dataset table, word embeddings, t-SNE/UMAP, tokenization, data augmentation); and AI for science (protein structure prediction, molecular graph neural network, cell segmentation, LLM chat assistant, GPU cluster).
Flat, minimal diagrams on a 512 × 512 canvas with one shared palette: navy and soft blue, warm orange, yellow and green. Every part sits in a named group so Recolor and Edit can target it.
Topics: machine learning, deep learning, artificial intelligence, ai, neural network, perceptron, activation function, relu, sigmoid, backpropagation, gradient descent, loss curve, dropout, cnn, convolution, lstm, rnn, transformer, attention, encoder decoder, autoencoder, gan, diffusion model, decision tree, random forest, svm, k-means, knn, regression, pca, cross validation, overfitting, confusion matrix, roc curve, precision recall, hyperparameter tuning, dataset, embeddings, t-sne, umap, tokenization, data augmentation, alphafold, protein structure prediction, graph neural network, cell segmentation, llm, chatbot, gpu cluster.
Neural network basics (7)
Artificial neuron (perceptron) · Pro
Fully connected (dense) neural network with an input layer, two… · Pro
Activation functions · Pro
Backpropagation · Pro
Training and validation loss curves decreasing over epochs · Pro
Gradient descent · Pro
Dropout · Pro
Architectures (9)
Convolutional neural network · Pro
Convolution · Pro
Recurrent neural network LSTM cell with forget, input and output… · Pro
Transformer block · Pro
Self-attention · Pro
Encoder–decoder (sequence-to-sequence) · Pro
Autoencoder · Pro
Generative adversarial network · Pro
Diffusion model · Pro
Classic machine learning (7)
Decision tree · Pro
Random forest · Pro
Support vector machine · Pro
k-means clustering · Pro
k-nearest neighbors · Pro
Linear regression fits a straight line to predict a value; logistic… · Pro
Principal component analysis · Pro
Training & evaluation (7)
Dataset split into training, validation and test sets (70 / 15 / 15) · Pro
k-fold cross-validation · Pro
Underfitting, a good fit and overfitting of the same data · Pro
Confusion matrix · Pro
ROC curve · Pro
Precision–recall curve · Pro
Hyperparameter tuning · Pro
Data & representation (5)
Dataset as a table · Pro
Word embeddings in a vector space · Pro
t-SNE / UMAP embedding · Pro
Tokenization · Pro
Data augmentation · Pro
AI for science (5)
Protein structure prediction · Pro
Graph neural network on a molecule · Pro
Cell image segmentation · Pro
Large language model chat assistant answering a question in a chat… · Pro
GPU compute cluster · Pro
License
Included with Sciicons Pro (one-time lifetime purchase). Use the illustrations in papers, theses, posters, slides, grant figures and teaching; editable SVG with named layers. Redistribution of the files as an icon set is not allowed. See how to cite Sciicons.
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