Deep Learning for Beginners with Python

Master Python conditional statements and loops, including if else, for, and while. Define and use functions, with print and return to display or pass values.

Explore numpy basics: create 1d and 2d arrays, inspect data types and shapes, and perform indexing, slicing, and statistical operations like mean, variance, std, and median.

Introduce Seaborn for data plotting, load datasets with Seaborn, and visualize iris sepal length via histograms. Customize with bins and hue, while Seaborn uses matplotlib behind the scenes.

Explore machine learning foundations, including supervised, unsupervised, reinforcement, and semi-supervised methods; learn classification, regression, and clustering with key algorithms, data preparation, model training, validation, and deployment.

Explore how regularization reduces overfitting, improves generalization, and balances loss with a complexity term in L2 regularization, controlled by lambda.

Code along in Python demonstrates loading the iris dataset from scikit-learn, standardizing features with StandardScaler, and implementing logistic regression, SVM, decision tree, and random forest to compare their accuracy.

Discover unsupervised learning with unlabeled data to uncover patterns and cluster data, using methods like k-means, hierarchical and density-based clustering, PCA and LDA, association rules, and dimensionality reduction.

Reinforcement learning learns from mistakes through trial and error, using rewards for correct actions, penalties for wrong ones, and aims to maximize rewards in autonomous vehicles, real-time games, and robotics.

Introduce neural networks, inspired by the nervous system, and describe their structure with input, hidden, and output layers, plus activation functions that create nonlinear models.

Explore the diverse applications of deep learning, including image classification, object detection, speech recognition, natural language processing, sentiment analysis, and autonomous driving.

Compare shallow and deep neural networks: shallow with one hidden layer is easy to build but limited for complex patterns; deep learns complex features with higher accuracy, though longer training.

Learn to use optimizers to speed up convergence and reach lower loss, including SGD, Adam, RMSProp, Adagrad, and mini-batch gradient descent and stochastic gradient descent methods, while avoiding local minima.

Learn to load a customer churn dataset and prepare features for a deep learning model, skipping unnecessary columns, splitting inputs (X) and target (Y), and visualizing class distribution.

Explore data visualization with a pair plot using seaborn to compare numerical features like credit score, tenure, balance, and salary, colored by gender, and identify patterns for customer exit.

Import TensorFlow as tf and Keras, using the sequential API as a container for flatten, input, and dense layers. Import Adam from TensorFlow.keras.optimizers and skip dropout to begin building model.

Explore building a neural network using sequential API and functional API, adding dense layers with relu activations and sigmoid output for binary churn prediction, and review model summary and parameters.

Compile and train a neural network with Adam, a 0.001 learning rate, and binary cross entropy loss; train for 10 epochs with batch size 32 and monitor accuracy.

Save and load a deep learning model by creating a model directory, saving the model as an .h5 file, and storing the preprocessing scalar with pickle.

Build a deep neural network with TensorFlow to classify fashion Mnist images (28×28) into ten classes, using 784 input neurons, hidden layers, and an output layer.

Download and explore the Fashion MNIST dataset from OpenML, noting its 10 classes, 60,000 training and 10,000 testing examples, and 28 by 28 images (784 features) for model training.

Analyze fashion mnist images by inspecting 28x28 pixels, separating input features and targets, visualizing samples across 0–9 classes, and training a deep neural network classifier on 60k/10k data.

MobileNet explains a lightweight architecture for mobile and embedded devices by using depthwise separable convolution and a pointwise convolution, reducing parameters dramatically with comparable accuracy.

Install and load the horses or humans dataset with TensorFlow dataset, using tfds.load, configure splits, info, and class labels for horse and human.

Examine the model layer by layer, noting convolution weights and biases while max-pool and flatten have no parameters. Train with Adam and cross entropy, observe overfitting, preview augmentation and regularization.

Plot training and validation loss and accuracy on a two-row subplot to assess overfitting and underfitting. Save and load the model using .h5 with keras load_model, demonstrating end-to-end model persistence.

Load a saved Keras model and prepare a four-dimensional, 300×300 image, then predict with a 0.5 threshold to distinguish horses from humans.

Explore regularization techniques to prevent overfitting in neural networks, including L1, L2, and early stopping, plus dropout, batch normalization, and data augmentation.

Dropout randomly drops neurons during training to prevent overfitting and encourage robust feature learning, while batch normalization normalizes inputs within batches to speed convergence.

Learn how to calculate the number of parameters in cnn and fcn layers using standard formulas, including bias terms, with notes on maxpooling and flatten layers having zero parameters.

Use image data generator with rescaling, normalization, and augmentation (rotation, shifts, zoom, horizontal flip) plus dropout to reduce overfitting and compare regularized CNN with baseline on cats versus dogs.

Monitor training results to adjust learning rate from 0.0001 to 0.001 to smooth the validation curve and reduce overfitting, then save the trained model for future predictions.

Design a convnet and compare it with transfer learning to measure time and accuracy, including a 0.2 validation split for five flower classes, using image data generator with 0-1 normalization.

Apply data augmentation and transfer learning with VGG16 to improve a CNN model for flowers classification, and fine-tune on augmented data to achieve higher validation accuracy.

Enable online prediction of flower classes by loading a trained model, fetching image URLs, preprocessing to 224 by 224, and predicting class probabilities.