Deep Learning Essentials
For engineers preparing for machine learning and AI interviews who want to answer deep learning questions with a clear short answer and the understanding behind it. You will be able to explain neurons, layers, loss functions, backpropagation, optimisers, CNNs, activation functions and regularisation, show what happens to the numbers with short PyTorch examples, and handle the follow-up questions.
Course Content
Prepares you to explain when deep learning is worth its cost over classical machine learning, what depth and capacity really buy, and where the convex hull idea shows up.
Prepares you to explain what a single neuron computes, what weights and biases store, and what adding layers does to a network's power and its training risks.
Prepares you to explain why deep networks overfit so easily, how to spot it on a training curve, and how dropout and L1/L2 penalties change the numbers during training.
Prepares you to explain why convolutional networks beat fully connected ones on images, and what a convolution actually computes on the pixels.
Prepares you to talk credibly about the deep learning tools you have used, and to explain tensors, their shapes, and the computational graph that makes automatic gradients possible.
Prepares you to name the main supervised, unsupervised and self-supervised deep learning models, and to explain how autoencoders, GANs, Boltzmann machines, encoder-decoders and ensembles work and when each is used.
Prepares you to trace an input through a network to a prediction, explain what the loss number means and why training needs it, and pick the right loss for classification and regression.
Prepares you to explain how perceptrons differ from multi-layer networks, name every part of a network, walk through one training step, and justify how weights are initialised and why they cannot start at zero.
Prepares you to explain why networks need non-linear activations, which one to use in hidden and output layers, and how sigmoid, tanh, ReLU and LeakyReLU change the gradients during training.
Prepares you to answer the CNN design questions that follow the basics: CNN versus RNN, Conv1D, 2D and 3D, which layers hold parameters, padding, pooling, and turning dense layers into convolutions.
Prepares you to explain how to stop a network memorising its training data: data augmentation, dropout versus batch normalisation, a step-by-step overfitting fix, the full list of regularisers, and the autoencoder bottleneck.
Prepares you to answer the practical questions that close most deep learning interviews: when and how to use transfer learning, how fine-tuning works, and which hyperparameters to tune first.