Data Science: Deep Learning and Neural Networks in Python

Review the prerequisites for the course and explore logistic regression as the building block of neural networks, showing how models make predictions and how to train them to measure error.

Explore how logistic regression learns weights through the binary cross-entropy loss, derives gradients, and applies gradient descent with learning rate, while considering regularization to prevent oversized weights.

Explore how to prepare e-commerce data for deep learning by encoding categories, normalizing numerical features, and building logistic regression and neural network models to predict user actions.

Explore how neural networks extend logistic regression to non-linear classification by combining multiple neurons, outline prediction and training, and apply softmax and sigmoid for multi-class and binary tasks.

Transition from logistic regression to neural networks through a one-layer feedforward network, using weights, biases, and sigmoid activations to compute outputs, and explore nonlinearities like tanh and relu.

Explore the equivalence of sigmoid and softmax for two-class problems and derive the softmax form. Advise using softmax for any K, as it covers the sigmoid case when K=2.

Explore feedforward neural networks with two inputs and three hidden units, predicting binary outcomes and softmax multiclass targets. Learn vectorized weight matrices, biases, and one-hot encoding for training labels.

Access all course code at github.com/lazyprogrammer/machine_learning_examples, with files like forwardprop.py, backprop.py, xor_donut.py, and tf_example.py in the ann_class folder. Run git pull to stay updated.

Practice making predictions with a neural network in an e-commerce project, using a forward pass, softmax, and random weights to generate and evaluate initial predictions before training.

Explore a digital suggestion box to gather targeted feedback on your background, course difficulty, and missing topics like CNNs, transformers, or quantum mechanics, improving Python explanations.

Mirror the big picture of backpropagation by centering on the cost function and gradient descent for multiclass predictions, not the calculus details, and translate the equations into Python code.

Learn to train multi-class logistic regression with a softmax output, using cross-entropy loss and gradient descent, and apply the chain rule to derive the required gradients for weight W.

Derive the gradient of the logistic regression objective with softmax and simplify using delta cancellation and dummy variables, then implement with X transpose times T minus Y via numpy dot.

the lecture extends backpropagation to a three-layer neural network, deriving gradients for W3, W2, and W1 via the chain rule and total derivatives.

Implement and train a multi-class neural network in Python, including data preprocessing, one-hot encoding, softmax cross-entropy loss, gradient descent, and evaluating train and test accuracy in the Colab notebook.

Explore gradient updates for binary classification with a one-hidden-layer neural network using a sigmoid output and binary cross-entropy.

Train neural networks by minimizing a cost function with backpropagation and gradient descent, using vectorized NumPy on two-dimensional data, and compare accuracies to logistic models on an e-commerce dataset.

Explore neural networks for regression, using a 100 hidden units feedforward model to fit a satel shape curve y equals x1 times x2, with gradient descent and residuals visualization.

Explore practical methods for selecting learning rate and regularisation penalties in neural networks, using log-scale tuning, batch-size normalization, and debugging strategies.

Learn to build neural networks in three lines of code with Sci-Kit learn using MLPClassifier for training and predicting, with API consistency across models.

Explore facial expression recognition using a 48x48 grayscale Kaggle dataset of seven emotions. Preprocess data from CSV, and compare neural network models by accuracy, complexity, and training time.

Build a neural network for facial expression recognition in Python using sigmoid and compare binary classification to logistic regression, detailing forward propagation, backprop, and training.

Derive the softmax derivative for backpropagation, computing with respect to a(k) and a(k′) to obtain y(k)−y(k)^2 and −y(k)y(k′). Use the delta function to simplify the sum and yield t_n(k)−y_n(k).

Explore common backpropagation issues and how careful calculus and indexing conventions clarify the derivatives of softmax and cross-entropy for weights W and V in hidden and output layers.

Clarifies the difference between neural networks and deep learning, debunks myths about CNNs and RNNs, and traces their evolution from perception classifier to modern architectures.