Beginners Guide to Deep Learning

Understand the neuron as the building block of deep learning, detailing its structure (dendrites, cell body, axon), synapses with weights, bias, and the weighted sum followed by activation.

Learn how neural networks form from interconnected neurons with input, hidden, and output layers, use forward propagation and backpropagation to learn features and classify data.

Explore how activation functions like threshold, sigmoid, softmax, and ReLU transform inputs into outputs in neural networks, enabling binary and multi-class classification in deep learning.

Explore tensors as the data structure in deep learning, from scalars to 4D tensors, covering axes, shape, dtype, backpropagation with gradient descent, slicing and batching, illustrated by mnist.

Explore data tensors across 2D, 3D, 4D, and 5D forms with concrete examples including actuarial data, word counts, stock prices, tweets, images, and videos.

Explore tensor operations used in deep learning, including element-wise operations, broadcasting, dot product, reshaping, and transpose, with practical NumPy demonstrations.

Explore how neural networks learn by optimizing trainable parameters, starting from random weights and biases, using batches of inputs to reduce loss through gradient descent and backpropagation.

Explore gradient optimization by deriving the derivative and gradient, applying gradient descent with momentum to minimize a loss function via backpropagation in neural networks.

Master backpropagation by applying the chain rule to compute neural network gradients from loss through the backward pass, leveraging symbolic differentiation tools like TensorFlow.

Explore a neural network example using Keras to classify 28 by 28 MNIST digits with two dense layers, a softmax output, and training with categorical cross-entropy and accuracy assessment.

Explore neural networks and core components like layers, objective functions, and optimizers, with a quick intro to Keras, and apply them to movie reviews, newswires, and house prices.

Explore the anatomy of neural networks: layers, input data, targets, loss, and optimizer, and see how topology and weights guide model design in Keras-style pipelines.

Explore how to set up Keras with TensorFlow as a backend and verify the TensorFlow version. Define training data and layers, and train models using the Sequential or functional API.

Classify movie reviews as positive or negative using IMDb data in Keras. Build a dense network with 10,000-dimensional vectors, train via binary cross entropy and RMSprop, and monitor validation.

Classifying newswires with a neural network: a multi-class text classification using the Reuters dataset to predict 46 topics, with 64 dense layers, softmax output, and categorical cross entropy.

learn to predict median house prices with regression using the Boston housing dataset, performing feature normalization, a small two-layer network, MSE and MAE losses, and k-fold cross-validation to curb overfitting.

Learn essential data pre-processing for deep learning, including vectorization to tensors, normalization to zero mean and unit variance, handling missing values, and feature engineering to improve model learning.

Reduce network size and apply weight regularization to control capacity and boost generalization, preventing overfitting with L1 or L2 penalties.

Learn how dropout layer acts as a regularization technique in neural networks by randomly zeroing out features during training and scaling outputs at test time to reduce overfitting.

Discover the deep learning workflow from defining the problem and data to choosing metrics, loss functions, and evaluation protocols, then prepare data, train, regularize, and finalize a production model.

Explore convolutional neural networks for computer vision and image classification, using data augmentation to combat overfitting and a pre-trained CNN for feature extraction.

Explore how convolutional layers in CNNs learn local patterns and translation-invariant features from patches, building hierarchical representations through filters and feature maps.

Max pooling in convolutional neural networks downsamples feature maps with 2 by 2 windows to halve dimensions and prevent overfitting by reducing feature counts, while average pooling provides another option.

Explore a convolutional neural network for mnist digit classification in a Keras workflow. The video shows a 3-conv, 2-pool architecture that flattens to a 10-class softmax classifier, achieving 0.98 accuracy.