Advanced Deep Learning With TensorFlow

Explore how convolutional neural networks mimic the brain's visual cortex to perform object recognition, using layered edge detection, pooling, flattening, and a final fully connected classifier.

Demonstrates the convolution operation in CNNs by applying a 3x3 kernel to a 6x6 input, performing elementwise multiplication, and summation, then sliding to generate the output.

Apply convolution with edge-detecting kernels to reveal horizontal and vertical edges, using a Sobel detector and zero normalization to produce a grayscale edge map.

Describe max pooling and average pooling in CNNs, sliding a 2x2 window to produce a reduced feature map by max or average values, preserving location invariance.

The convolutional layer applies kernels to 2d input image with padding, producing feature maps after activation; kernels detect edges and parts, followed by max pooling and flattening into classifier.

The lecture explains LeNet, a CNN for character recognition, with a 32x32x3 input, two conv-pool stages, flattening to 400, followed by 120, 84, and a 10-class softmax.

Apply data augmentation to build invariant CNNs that classify objects across orientations while expanding the dataset with transformations such as scaling, flipping, rotation, translation, and noise to improve model tuning.

VGG16 is a 16-layer convolutional network from Oxford's Visual Geometry Group, trained on ImageNet with 1.2 million images, using 3x3 convs with same padding and 2x2 pooling for 1000-class output.

Transfer learning uses pre-trained models, such as a 16-layer CNN trained on millions of images, to classify new tasks like cats and dogs by freezing or fine-tuning layers.

Explore why recurrent neural networks excel for sequence information, where multilayer perceptrons fail, with applications in time series, machine translation, speech recognition, and image captioning.

Explore the box representation of recurrent neural networks, showing how an input word and previous outputs combine through weight matrices and activation functions to preserve sequence information over time.

Learn the three main rnn types and a simple mlp baseline: many-to-one, many-to-many, and one-to-many, with examples like sentiment classification, machine translation, and image captioning.

Explore how to train a recurrent neural network using backpropagation through time, addressing forward passes, loss calculation, and weight updates across time steps to mitigate vanishing gradients.

Explain why long short term memory units address long distance dependencies by short-circuiting connections, enabling more effective backpropagation through time in sequence tasks like translation.

Explore lstm notations and rnn box representations, showing how inputs, previous outputs, and final outputs combine via concatenation, pointwise multiplication, and addition with sigmoid-activated layers.

Explore how LSTM cells control information flow with input and forget gates, enabling short-circuiting of dependencies and preserving long-term memory in sequences.

Explore the LSTM unit, detailing the forget gate, input gate, and output gate that control information flow and shape the cell state and final output.

GRU has only four equations and two gates, making it simpler and faster to train. Its short-circuiting via the update and reset gates helps maintain long-term dependencies.

Explore how deep recurrent neural networks extend single-layer RNNs by stacking layers that unfold over time along a time axis, and learn via forward and backpropagation through time.

Explore bidirectional RNNs that process sequences in forward and backward directions, concatenate their outputs, and leverage future context for improved sequence modeling in NLP.

Discover what TensorFlow is as an open-source machine learning and deep learning library from Google Brain, and explore tensors, constants, and multi-dimensional arrays stored in memory on Google Colab.

Explore Google Colab for writing and executing Python code and machine learning code in a browser with zero configuration, using Google's GPUs, sharing notebooks, and collaborating across data scientists.

Learn how to create tensors of constants in TensorFlow across 0-D to 4-D dimensions and shapes, exploring immutable elements and tensor shaping.

Learn the difference between constants and variables in tensors, showing how constants are immutable and variables unlock mutability by indexing and modifying tensor contents.

Learn how to create random tensors for neural network initialization, explore pseudo random numbers, seeds for reproducibility, and using normal distributions with specific shapes.

Create tensors of zeros and ones in TensorFlow by specifying shape, such as a 4x4 array, and using the zeros and ones methods to initialize values.

Learn to create tensors from numpy arrays by converting a range from 1 to 20 into a dense tensor, illustrating element shapes and the transition from arrays to tensors.

Explore how to extract information from tensors in TensorFlow, including shapes, dimensions, rank, and element counts, and learn to access multi-dimensional elements through practical examples.

Explore tensor slicing to extract a 2x2x2 sub-tensor from a 3x3x3 tensor by selecting two elements along each dimension using start and stop indices.

Learn to slice tensors in Python using colon notation to set start and end indices across 2-D and 3-D tensors, including all elements when needed and controlling dimension ranges.

Learn to extract the last column from a 3d tensor by selecting the final elements along each axis. With start and end positions to include all elements.

Learn to extract the last rows from a tensor across multiple dimensions by using slice rules and explicit start and end indices to select the final elements.

this lecture demonstrates basic tensor arithmetic—addition, subtraction, and multiplication—using element-wise operations and constants, and shows how tf.multiply reproduces results in the TensorFlow library.

Learn how to multiply two matrices by matching inner dimensions and compute the 2x3 result from dot products of each row with each column.

Demonstrates multiplying a 2x2 and a 2x3 matrix in Python, verifying the result against a direct calculation and a TensorFlow operation, then hints at reshaping for mismatched dimensions.

Learn how to transpose and reshape matrices in TensorFlow, convert between two by three and three by two shapes, and perform matrix multiplication by aligning dimensions.

Explore linear regression with neural networks by predicting prices from features like bedrooms, bathrooms and parking, using mean squared error and mean absolute error to tune w1, w2, and w0.

Navigate the regression neural network architecture with input, hidden, and output layers, and grasp activation, loss, and hyperparameter tuning, including stochastic gradient descent and another optimizer.

Define a neural network architecture by selecting the input layer, hidden layers, and output neurons, then compile with a loss function and optimizer and train to learn patterns in TensorFlow.

Build a simple neural network with TensorFlow using a sequential model to learn a linear regression from a toy dataset; adjust layers, neurons, and learning rate to improve performance.

Train a neural network regression model to predict insurance cost from age, sex, body mass index, region, smoker status, and children, using one-hot encoding, normalization, and a train-test split.

Learn classification by mapping patient features like sugar level and blood pressure to binary or multiclass labels, train neural networks with 80/20 data splits.

Learn to build a binary diabetes classifier with an eight-feature Pima Indians input, a sequential neural network, and an 80/20 train-test split, using a sigmoid output.