TensorFlow for Deep Learning Bootcamp

Explore what deep learning is, a subfield of machine learning based on artificial neural networks, and begin hands-on coding with TensorFlow, focusing on inputs and outputs.

Understand why deep learning suits complex, changing problems and large data, and how neural networks enable learning from structured and unstructured data.

Explore neural networks as deep learning algorithms that turn data into numbers, learn patterns in hidden layers, and produce labeled outputs through supervised, semi-supervised, unsupervised, and transfer learning.

Explore deep learning use cases such as recommendations, translation, speech recognition, computer vision, and natural language processing, and learn to turn inputs into numbers to find patterns with TensorFlow.

Discover how TensorFlow enables end-to-end deep learning. Build neural networks, preprocess data, and deploy models on GPU, CPU, or TPU with transfer learning via TensorFlow Hub.

Explore how a tensor encodes inputs, such as images, text, or sound, as they pass through a neural network to learn patterns and produce outputs in TensorFlow.

Cover data preprocessing, building and training deep learning models, and using pretrained models with TensorFlow Hub. Learn to fit, evaluate, save, load, and predict on unseen data through experimentation.

Learn to approach this course by coding many exercises, exploring and experimenting, visualizing concepts, asking questions, and sharing your work to master deep learning.

Explore the fundamentals of tensors in TensorFlow by creating constants in Colab, differentiating scalars, vectors, matrices, and tensors, and examining shapes, dimensions, and data types with tf.constant.

Explore creating tensors with tf.Variable and tf.Constant in TensorFlow, learn about changeable versus unchangeable tensors, and practice indexing and assignment to observe value changes.

Discover how to create random tensors in TensorFlow using random generators, including uniform and normal distributions, seeds for reproducibility, and the role of initialization in neural networks.

Learn how to shuffle tensors along the first dimension to mix data and prevent biased learning, using tf.random.shuffle and seeds for reproducible results.

Create tensors from numpy arrays with tf.constant, and use ones, zeros, and reshaping to manage shape, dimensions, and reproducible randomness via global and operation seeds.

Learn to extract information from tensors by using shape, rank, axis, and size to understand and manipulate data. Index into tensors, print attributes, and practice gpu-accelerated workflows.

Index and expand tensors in TensorFlow by extracting elements, understanding shapes and dimensions, and using new axis or expand_dims to add dimensions for neural networks.

Master basic tensor operations in TensorFlow, including addition, subtraction, multiplication, and division on tf constants, while keeping originals intact and preparing for matrix multiplication.

Learn how matrix multiplication differs from element wise operations by applying the dot product to tensors, ensure inner dimensions match and result follows inner shape, using tf and python.

Explore matrix multiplication with tensors, focusing on inner-dimension compatibility, dot product rules, and how reshaping or transposing affects output shapes; apply practical examples to understand tensors in neural networks.

Master matrix multiplication and the dot product with tensors using tf dot and tensor dot, including transpose, reshape, and detecting silent shape errors.

Explore how to change tensor data types in TensorFlow, from float 32 to float 16, using casting, and understand how reduced precision saves memory and speeds computation.

Practice tensor aggregation in Google Colab with TensorFlow by computing absolute values, minimum, maximum, mean, and sum using reduce operations on random tensors.

Explore how to update tensor datatypes, compute tensor variance and standard deviation, and apply reduce variance techniques with TensorFlow Probability.

Learn to find the positional maximum and minimum in tensors with argmax and argmin, compute variance via tf.math.reduce_variance, and compare with standard deviation through practical examples.

Learn how to squeeze a tensor by removing all dimensions of size one, using the squeeze method to reduce its shape to its essential form.

Learn how to apply one-hot encoding to tensors to convert categorical data into numerical form for neural networks, including practical examples with depth, indices, and on/off values.

Practice with tensor math operations in TensorFlow, including squaring, square roots, and natural logarithms, while learning how to handle data types and cast to float for compatibility.

Discover TensorFlow and NumPy interoperability by converting between NumPy arrays and tensors, using constants, and checking dtypes. Understand default float32 vs float64 and how type differences impact conversions.

Explore how TensorFlow tensor operations run on GPUs for fast numerical computing, compare CPU, GPU, and TPU performance, and enable free Google Colab GPU access to automatically accelerate your code.

Learn to build neural network regression models with TensorFlow, including architecture, input/output shapes, features and labels, and training, evaluating, saving, and loading models.

Explore the inputs and outputs and architecture of a neural network regression model, including input features, one-hot encoding, and output shapes, as you predict house sale prices using supervised learning.

Explore the anatomy of neural networks for regression, detailing input, hidden, and output layers; configure hyperparameters, loss, and optimizers to build TensorFlow regression models.

Create and visualize simple regression data in Colab using TensorFlow, mapping input features X to outputs Y to illustrate a regression problem and prepare data shapes for modeling.

Explore the steps of modelling with TensorFlow: create a model, compile it with a loss function and an optimizer, then fit it to learn the relationship between X and Y.

Explore how to improve a neural network by adjusting the creation, compilation, and fitting steps in TensorFlow, including layers, hidden neurons, activation functions, loss, optimizer, training data, and epochs.

Compare a smaller versus larger TensorFlow model by adding hidden layers and using the Adam optimizer with a learning rate, then train epochs to reduce absolute error and improve predictions.

Experiment with small, targeted changes in a TensorFlow model—adjust hidden layers, units, activation, optimizer, and learning rate—then evaluate on unseen data to avoid overfitting.

Master a practical TensorFlow workflow to build, fit, evaluate, and tweak deep learning models, and visualize data, training, and predictions against ground truth; tune hyperparameters.

Learn to split data into training, validation, and test sets in TensorFlow, then train and evaluate a neural network to generalize to unseen data.

Build a simple Keras sequential neural network with one dense layer, define the input shape, and train on training data. Then inspect the model summary for layers and trainable parameters.

Experiment with hidden units to observe changes in total and trainable parameters, and visualize the model's layers with plot model while checking input and output shapes.

Visualize model predictions against ground truth using a scatter plot of test data versus predictions, and prepare to evaluate performance with regression metrics in the next video.

Learn to evaluate regression models in TensorFlow for deep learning bootcamp using mean absolute error and mean square error, and when to apply each on the test set.

Explore evaluating a TensorFlow regression model using mean absolute error, aligning y_true and y_pred shapes, and applying squeeze to ensure correct tensor formats during metric calculation.

Explore calculating mean squared error for TensorFlow regression, compare it with mean absolute error, fix shape issues with tf.squeeze, and create reusable helper functions for MAE and MSE.

Design and run three modeling experiments to improve a simple TensorFlow model, using reproducible seeds, training for varied epochs and layer counts, and evaluating with MAE and MSE.

Build and compare TensorFlow modelling experiments by expanding from model two to model three, adding a dense layer and more epochs, then evaluate predictions and discuss overfitting as future topic.

Compare your TensorFlow modeling experiments by organizing results in a pandas data frame, start small, and track outcomes with TensorBoard or weights and biases.

Save a TensorFlow model using the saved model format or the HD five format to export for web or mobile apps, then load it to resume training.

learn to load a saved model in both the saved model format and the H5 format using the load_model method, then compare predictions to verify equivalence.

Explore three practical methods to download and save files from Google Colab: using the files tab, using code to download, and saving to Google Drive via mounting.

Prepare a dataset for a regression model using the Kaggle medical costs data, apply one-hot encoding with pandas get_dummies, and define features like age, BMI, smoker, region to predict charges.

Build a regression model by creating X and y from features and labels, performing a train-test split, and training, compiling, and evaluating a TensorFlow model with mean absolute error.

Experiment with adding an extra layer and more hidden units, train longer with Adam, and use early stopping to evaluate improvements on the test set.

Learn how normalization and standardization preprocess data for neural networks, focusing on scaling features with min-max normalization and standard scaling. See how scaling improves convergence and model performance in practice.

Normalize numerical features to 0-1 with a column transformer, apply one-hot encoding to categorical fields, then train a neural network on the normalized insurance data.

Demonstrates fitting a neural network on scaled data, reproducing a three-layer neural network, and comparing normalized versus unnormalized inputs to show faster convergence and about 30 percent error reduction.

Explore neural network classification with TensorFlow, covering binary, multiclass, and multilabel problems, model building steps, data preparation, evaluation, and saving workflows.

Turn food images into numerical tensors, normalize pixel values, and run them through a classifier (often with transfer learning) to predict sushi, steak, or pizza.

Learn how image classification data are encoded into input and output tensors, and how shapes like batch size and 224 by 224 by 3 enable multiclass predictions.

Explore the typical architecture of a classification model, focusing on input and output shapes, hidden layers and activations, and choosing loss functions and optimizers for binary and multiclass tasks.

Create a toy classification dataset using make circles in Colab, label data for binary classification, and explore data frames and plots to prepare a model.

Check input and output shapes for binary classification, prepare data as tensors, and build and compile a simple TensorFlow model.

Build a binary classification model in TensorFlow, starting with one hidden layer and adding a second. Compile with binary cross-entropy and SGD, fit for several epochs, and evaluate with accuracy.

TensorFlow for deep learning bootcamp demonstrates improving a binary classifier stuck at about 50 percent accuracy by adding layers, more hidden units, and longer training with Adam.

Visualize data and model predictions to diagnose performance, then implement a plot_decision_boundary with a mesh grid to compare predictions against circular red and blue data, exploring binary and multiclass classification.

Explore adapting a linear classification model for regression in TensorFlow by switching from binary cross entropy to regression losses like mean absolute error, and visualize data, predictions, and model behavior.

Explore neural network non-linearity by testing a simple one-hidden-neuron model with linear activation on linear and nonlinear data, visualizing decision boundaries and training behavior.

Explore non-linear activation to model non-linear data with a hidden layer neural network, using Relu, binary cross-entropy, and the Adam optimizer, and experiment with layers and neurons.

Upgrade a non-linear model by adding hidden layers and non-linear activation in the TensorFlow playground to distinguish orange and blue dots, using a sigmoid output for binary classification.

Model non-linear data with a binary classifier using a sigmoid output, binary cross-entropy loss, and Adam, training a neural network to reveal the circle's decision boundary.

Explore the impact of non-linear activation functions like sigmoid and ReLU on toy tensor data, demonstrate how nonlinearities enable pattern learning, and compare with linear activations.

Tune the learning rate to accelerate learning and improve generalization in neural nets, via training/test splits, Adam optimizer, and binary cross-entropy for fast, accurate classification.

Learn to use TensorFlow's history object to plot training loss curves, analyze accuracy, and compare models via visual loss trends and learning-rate effects.

Use a learning rate callback to find the ideal learning rate that minimizes loss during training, by plotting loss against learning rate with a scheduler.

Explore finding the ideal learning rate through learning rate scheduling, compare models on training and test sets, and visualize binary classification decision boundaries to evaluate model performance.

Explore common classification evaluation metrics, including accuracy, precision, recall, f1 score, and confusion matrices. Understand trade-offs between precision and recall, especially in imbalanced datasets.

Learn to evaluate deep learning classifiers using accuracy, precision, recall, F1 score, and confusion matrices, with practical coding in TensorFlow and classification reports on test data.

Create your first confusion matrix to diagnose model errors by comparing true and predicted labels, identifying true positives, true negatives, false positives, and false negatives, and converting probabilities to binary.

Learn to prettify a confusion matrix by adapting a plot_confusion_matrix function, normalize results, and customize labels, colors, and annotations on the figure for binary and multiclass models.

Explore multiclass classification with a fashion-mnist dataset, preparing data in TensorFlow, and train a neural network to distinguish ten clothing classes like shoes, shirts, and jackets.

Convert numeric labels to human readable class names for a ten-class grayscale multiclass dataset. Visualize random samples and plot multiple examples to understand the data before building a neural network.

Build a multiclass classification model by adjusting input shape, using a one-per-class softmax output, and applying sparse or categorical cross entropy loss. Flatten inputs 28 by 28 and train with Adam.

Normalize input data to the 0–1 range to improve a ten-class multiclass classifier; using the same model with normalized data significantly boosts validation accuracy from about 35% to 70–80%.

Compare two models with the same architecture trained on non-normalized versus normalized data, noting faster loss decrease and lower starting loss when neural network features are scaled.

Find the ideal learning rate for multiclass classification by plotting loss across learning rates, using Adam, Softmax, and sparse categorical cross entropy on normalized 28x28 inputs.

Evaluate a multiclass classification model by building a confusion matrix, analyzing predictions and probabilities, and refining with data normalization, training choices, and model saving for reuse.

Create and visualize a confusion matrix to evaluate multi-class predictions, compare true labels with test labels using class names, interpret misclassifications, and guide model improvements.

Visualize random predictions for a multi-class classifier by plotting a random image with its true and predicted labels, using a reusable plotting function.

Explore how neural networks learn patterns across layers, from input to output, by inspecting weights and biases in a 14-layer model and visualizing learned representations.

Explore convolutional neural networks and computer vision with TensorFlow by building Food Vision to classify pizza, steak, and sushi. Learn CNN architecture and end-to-end binary and multiclass image classification.

Explore how a convolutional neural network in TensorFlow processes 2d images with conv layers, pooling, and a sigmoid output for binary classification, illustrated by pizza versus steak.

Build a convolutional neural network for a food vision model in Colab by downloading and preprocessing food 101 dataset, narrowing to pizza and steak, and preparing training and test data.

Become one with the data by inspecting and visualizing many samples before coding, especially for computer vision.

Learn to inspect image directories, visualize random samples with matplotlib, derive class names from subdirectories, and preprocess 512x512 color images into tensors for convolutional neural networks using TensorFlow.

Learn how images are read into 512x512x3 tensors and normalized to the 0–1 range, and build an end-to-end CNN in TensorFlow with convolution, pooling, and fully connected layers.

Build an end-to-end convolutional neural network by loading and preprocessing images with normalization, using image data generators for train and validation data, and training a binary classifier with cnn.

Enable GPU acceleration on Google Colab to run CNN training faster, cutting epoch times and boosting validation accuracy toward 90% for a pizza versus steak classifier.

Experiment with a non-cnn model on image data, compare dense networks to convolutional ones, tune relu and sigmoid activations, and evaluate training with gpu acceleration to beat validation accuracy.

compare a non-cnn dense model with a convolutional neural network, add a layer and more units to model three, and observe cnn outperforming the dense model in computer vision.

Learn how convolutional neural networks learn key image features more effectively than dense nets, using a binary pizza vs. steak dataset, with visualization and data preprocessing.

Prepare image data for a convolutional neural network by normalizing pixel values to 0–1, splitting into training and test sets, and creating 32-image batches via an image data generator.

Load and preprocess image data for a cnn with ImageDataGenerator, configuring target size and color mode, flow from directory, and binary class mode, producing training and test batches ready.

Build a baseline three-layer convolutional neural network for image classification, covering data preprocessing into batches, input tensors, conv layers, optional pooling, and why starting simple guides improvement.

Discover how a Conv2D layer uses 10 filters with a 3x3 kernel, stride 1, and padding options like same or valid to learn features via sliding windows from pizza images.

Compile and fit a baseline cnn with binary cross entropy and Adam, training over five epochs on 47 training batches and 16 test batches, achieving about 88 percent accuracy.

Evaluate CNN training curves to assess learning in a binary classification task, plot training and validation loss and accuracy, and identify overfitting to guide experiments and improvements.

Explore how max pooling reduces overfitting in convolutional neural networks by condensing features and improving validation accuracy, while building a baseline, using regularisation, and applying data augmentation.

Explore data augmentation for CNN training, expanding data diversity with rescaling, rotation, shifts, and flips to reduce overfitting and improve generalization.

Examine and visualize how data augmentation reshapes training data using image data generator, flow from directory, and augmented batches; compare original and augmented images to understand generalization.

Learn to train a cnn on augmented data, observe on-the-fly data augmentation and shuffle effects, and experiment with model structure to reduce overfitting.

Explore how shuffling augmented training data improves CNN performance, compare training and validation accuracy, and apply data augmentation and robust data handling for deep learning models.

Explore ways to improve our convolutional neural network by adding more convolutional layers and filters, increasing epochs, and using augmented shuffled training data with transfer learning and Adam tuning.

Train a neural network for a food vision app and make predictions on your own custom data, using the pizza and steak classes and an uploaded image.

Write a helper function to load, decode, resize, and scale custom images to the model’s input format. Ensure preprocessing matches the training data for reliable predictions.

Load and preprocess a custom image, run it through the trained CNN to get prediction probabilities, map to class names, and visualize the predicted pizza or steak.

Extend binary pizza-vs-steak CNN to multiclass classification with ten food classes. Prepare data, build a baseline model, overfit to verify learning, then evaluate and iteratively adjust to improve performance.

Prepare multiclass image data for a CNN by normalizing and batching tensors with rescale, target size 224x224, batch size 32, and categorical labels for ten classes, build a CNN model.

Build a multi-class cnn model with 10 classes by configuring convolutional layers, max pooling, flattening, and a dense output with softmax, then compile with categorical cross entropy and accuracy.

Fit a multi-class CNN on ten classes with batches and five epochs, tracking training history, validation performance, and GPU acceleration.

Evaluate a multi-class CNN on a ten-class food dataset to measure accuracy and identify overfitting. Analyze training versus validation loss curves, and adjust hyperparameters to improve generalization and beat baseline.

learn how to tackle overfitting in a multi-class cnn by simplifying the model, removing layers, and applying data augmentation to improve generalization on unseen data.

Create an augmented image data generator to fight overfitting, train a model on augmented data, and compare to the baseline, showing improved validation accuracy and closer train–validation loss curves.

Experiment with architectures, data augmentation, learning rate, and epochs to improve a multiclass cnn, and explore transfer learning with custom images from the 10 food classes in Colab.

Learn to make predictions with a trained multiclass cnn on custom images such as pizza dad, hamburger, sushi, and steak pig, using class names, and save the model.

Save and load your trained CNN model for pizza versus steak binary classification, evaluate it on test data, and compare results to ensure consistency.

Explore transfer learning with TensorFlow, focusing on feature extraction and using pre trained architectures like ImageNet to improve a food vision app with less data, via hands-on code.

Apply transfer learning to reuse a pretrained architecture for feature extraction with 10 percent of training data, and set up data pipelines using image data generators on Colab with gpu.

Explore callbacks in TensorFlow, adding functionality during training and evaluation, and implement a custom TensorBoard callback to track experiments with model checkpoints and early stopping.

Explore TensorFlow Hub to access pretrained models for image classification, learn to use efficient net feature vector models, and practice one-line setup and feature extraction for transfer learning.

Build and compare two pre-trained TensorFlow Hub models for image classification using feature extraction and transfer learning, with a frozen hub extractor and a dense output layer.

Explore transfer learning with a 50-layer feature extractor, freezing pretrained ImageNet layers, and adapting a dense output to ten classes, achieving higher accuracy with only 10 percent of the data.

Plot and evaluate transfer learning resnet feature extraction model with matplotlib, visualizing training and validation loss, note overfitting, and compare to efficient net be zero tensor hub feature extraction model.

Explore building and training a pre-trained efficient net be zero feature extractor model, applying transfer learning on 10% of data to achieve 86% validation accuracy.

Explore three transfer learning types—as is, feature extraction, and fine tuning—by comparing pretrained efficient net be zero trained on image net, noting frozen base layers and trainable output layers.

Explore transfer learning and feature extraction with TensorBoard to compare model results, log experiments, and visualize training and validation performance across multiple models.

Address imposter syndrome as a natural part of learning, and transform breaks into practice by teaching others on Discord, using questions to gain experience through repetition and kindness.

Explore transfer learning in TensorFlow with fine tuning, unfrozen top layers, and adapting pretrained models to your data, while contrasting feature extraction and fine tuning.

Learn to streamline deep learning workflows by importing a reusable helper script into Colab. Access utilities like plot loss curves and training helpers without rewriting code.

Explore using tf applications to apply pre-trained models and load images from a directory into a TensorFlow batch dataset for multiclass food classification, with training and testing splits.

Explore five modelling experiments using a baseline EfficientNet B0 feature extractor, data augmentation, and selective fine-tuning, while transitioning from the sequential to the functional API.

Compare the TensorFlow Keras sequential API with the functional API, showing they compile and fit the same for simple models, while the functional API offers greater flexibility for complex architectures.

Build your first functional API model with transfer learning, using a pre-trained EfficientNet B0 base, global average pooling, and a custom top for a 10-class classifier.

Demonstrates compiling a functional API model, training with categorical cross entropy and the Adam optimizer, and applying transfer learning with an efficient net base to extract a feature vector.

Learn how to obtain a feature vector from a trained model by applying global average pooling to a base model’s output, illustrating feature extraction and transfer learning concepts.

Explain the feature vector as the learned, compressed representation of input data produced by a pre-trained model, then surfaced through a pooling layer to feed a dense classifier.

Explore transfer learning with the functional API in TensorFlow to compare 1% data feature extraction, data augmentation, and fine-tuning on Food 101's 10 classes.

Learn how to build a data augmentation layer inside your TensorFlow model, using tf.keras.layers preprocessing to apply random flips, rotations, zooms, and rescaling during training for GPU-accelerated augmentation.

Explore visualizing a data augmentation layer to compare original and augmented images, showing how GPU-accelerated on-layer augmentation increases training data diversity for robust feature extraction with transfer learning.

Execute transfer learning with one percent of the data, using a data augmentation layer. Build a model on a pre-trained EfficientNet base with a dense output layer.

Build model two with a feature extraction transfer learning approach using 10 percent of the data and a data augmentation layer, using an EfficientNet backbone for multiclass classification.

learn to use a model checkpoint callback to save weights or the full model during training, set a checkpoint path, monitor validation loss, and resume training across epochs.

Apply the model checkpoint callback to save weights during training, compare model two with model zero, and plot loss curves to assess fine-tuning of the feature extraction backbone.

Load weights from a checkpoint into model two, overwrite its weights, and evaluate to confirm results match the saved checkpoint. Use numpy.isclose to compare near-identical results despite floating-point precision.

Fine-tune model three by unfreezing the top layers of the EfficientNet-B0 base and training with 10 percent of the data from a saved checkpoint.

Fine-tune the last ten layers of the base model for five more epochs on 10 percent data, boosting validation accuracy from 81.2% to 84.3% in a transfer learning workflow.

Compare training histories to evaluate fine-tuning gains, using loss and accuracy plots before and after fine-tuning. Demonstrate how adding data influences training and validation performance and potential overfitting.

Download and unzip ten food classes from food 101 data set, prepare train and test datasets, and fine tune a model on all data for Model 4.

Revert a fine-tuned transfer learning model to feature extraction, reload from checkpoint, and compare performance when training on 10 percent versus all data with only the top 10 layers trainable.

Fine-tune a model on 100% of the training data to push validation accuracy past 90%, using transfer learning with feature extraction, selective unfreezing, and full-data evaluation.

Compare five trained models using TensorBoard to visualize histories, analyze transfer learning and fine-tuning results, and share experiment insights via TensorBoard.dev while tracking accuracy and loss.

View and manage your TensorBoard experiments by logging in, then identify each run by its ID and delete unwanted experiments from Colab to protect private data.

Scale up transfer learning to 101 food classes with food vision mini, training feature extraction then fine-tuning on 10 percent of food 101 data, and evaluating predictions on custom images.

Scale transfer learning with TensorFlow in Colab by loading Food 101 data, 101 classes, and 10 percent of training data, using helper functions and data loaders.

Train a large food-vision model with transfer learning on 10 percent of 101 classes using an efficient net zero backbone, featuring a model checkpoint callback, data augmentation, and five-epoch fine-tuning.

Create a data augmentation layer integrated into a TensorFlow model, running on the GPU, using preprocessing layers and a sequential headless backbone with random flip, rotation, and zoom.

Create a headless EfficientNetB0 transfer-learning model with built-in data augmentation by freezing the pretrained backbone, adding a global average pooling feature extractor, and a 101-class softmax output.

Beat the 101-class food vision baseline by fitting a transfer learning model with categorical cross-entropy, using Adam optimizer and accuracy metrics, evaluating on 15% validation and full test data.

Unfreeze the last five layers of the base model to begin fine-tuning, recompile with a reduced learning rate, and gradually unfreeze more layers to control overfitting.

Fine-tune a feature extraction model by unfreezing base layers, training for ten epochs, and comparing histories to balance training and validation performance, while using checkpoints to avoid overfitting.

Save and load your fine-tuned image classification model trained on 101 classes from 10 percent of data, export to Google Drive, and verify performance by loading and comparing results.

Download a pretrained model, load it, and evaluate its predictions on test data, then visualize results to assess accuracy across 101 food classes.

Apply softmax-based multiclass predictions to a trained TensorFlow model, interpret prediction probability arrays, and evaluate predictions against true labels for twenty-five thousand two hundred fifty test samples.

Evaluate model predictions by unraveling the test batch to extract true labels, convert one-hot labels to indices with amax, and compare them in the same order (shuffle off).

Verify the model's predictions align with the test labels by ordering, then compute the accuracy score and discuss evaluating metrics and confusion matrices.

Create a confusion matrix for a 101-class model using a helper function, adjust the x-axis labels to print vertically for readability, and preview classification metrics with the classification report.

Generate and interpret per-class metrics with a classification report, including precision, recall, and F1. Visualize class-wise F1 scores and compare accuracy, macro, and weighted averages to assess multi-class models.

Plot per-class F1 scores for 101 food classes with a horizontal bar chart using matplotlib, label class names, and explore per-class precision and recall insights for model tuning.

Create a load and prep image function that reads, decodes with tf.io.decode_image, resizes to the model input, and scales pixels for efficient net predictions on test and custom images.

this lecture guides you through loading and visualizing random test images, running model predictions with probabilities, and comparing them to true labels to evaluate the classifier.

Explore identifying the most wrong predictions by high-probability errors, then build a workflow to collect image paths, ground truth labels, predictions, and probabilities for visualization and data improvement.

Demonstrates how to identify the model's most wrong predictions by building a dataframe of image paths, ground-truth labels, and predictions, then visualize top high-confidence errors.

Plot and visualize the samples where the model's predictions are wrong but have high probability, using a 3x3 grid to inspect misclassifications and improve the model.

Test and visualize predictions on your own custom images by uploading photos in Colab, using the big dog model, and plot results to interpret classification outcomes.

Explore milestone project 1 food vision big in the TensorFlow for deep learning bootcamp, using the datasets module, preprocessing, batching, and mixed precision training in Colab.

Choose a gpu with compute capability 7.0 or higher for mixed precision training, preferably a Tesla T4 in Google Colab; if unavailable, you can still run but slower.

Import a prebuilt helper functions script from GitHub to streamline notebooks, load and prepare images, and track model experiments with a board callback for history comparisons.

Learn to use TensorFlow datasets (tfds) to download and load ready-to-use benchmarks like Food Vision and Food 101, with train and validation splits and metadata.

Explore Food101 from TensorFlow datasets by inspecting features, class names, image shape and data types, and practicing visualization and preprocessing to feed tensors to a neural network.

Build a pre_process_img function that resizes images to two-to-four by two-to-four, converts uint8 to float32, and returns image-label pairs from directory data for batching; efficient net models have built-in rescaling.

Learn how to batch and prepare image datasets with a data pipeline, applying a preprocessing function, mapping with parallel calls, shuffling, batching, and prefetching to speed up GPU training.

Discover how batching, shuffling, and prefetching data accelerate TensorFlow training by using 32-element batches, multi-threaded CPU loading, and GPU computation, with preprocessing steps and map functions.

Create modeling callbacks to log training results, save progress with model checkpoints during feature extraction and fine-tuning, and use tensorboard to compare model performance.

Enable mixed precision training in TensorFlow to accelerate model training by using float16 and float32 together, with a compatible GPU like Tesla T4 on Colab.

Apply mixed precision training to speed up a feature extraction model on modern GPUs. Freeze the base, use global average pooling, and keep a float32 softmax output for numerical stability.

Check each layer's dtype and dtype policy to verify mixed precision training, layer by layer, and observe how global policy enables float16 while keeping necessary layers in float32.

Learn to build a feature extraction model using efficient net pretrained on image net for 101 food classes, freezing base layers and later fine-tuning with mix precision and callbacks.

Build and fine-tune a feature extraction model to beat the DeepFood paper using transfer learning and precision training. Evaluate with the ten's board callback and the Milestone Project One template.

Explore natural language processing and sequence problems, covering text classification, text generation, machine translation, and sequence-to-sequence modeling with tokenization, embeddings, and RNNs.

Explore example inputs and outputs for a tweet-based disaster detector. Convert text to numerical encodings with tokenization and embeddings, then apply recurrent or convolutional networks to train and evaluate.

Explore the typical architecture of a recurrent neural network for text classification, detailing input, text factorization, embedding, a recurrent layer, and a sigmoid output to classify disaster tweets.

Set up a Colab notebook with a GPU, import helper functions, and load the disaster tweets dataset to kick off an NLP with TensorFlow project.

Visualize text data to build a mental model, load samples with pandas, and explore text and target labels while shuffling and splitting into train and test sets.

Split the training data into training and validation sets using train_test_split with 10 percent for validation and random_state 42, ensuring text and label pairs remain aligned.

Explore how tokenization converts text into numbers using word-level, character-level, and subword approaches. Learn embeddings as trainable vector representations and compare with one-hot and pre-trained word embeddings.

Set up a TensorFlow text vectorization layer to convert text to numbers by tokenizing, indexing tokens, and padding sequences to a fixed length.

Map text to numbers with a text vectorization layer by adapting to training data, handling unknown tokens, and padding sequences to a fixed length for deep learning and embedding.

Explore turning tokenized text into embedding vectors with TensorFlow's embedding layer, mapping vocabulary tokens to 128-dimensional dense representations for fixed-length sequences.

Start modeling experiments on text data by turning sequences into numbers, build tf-idf naive Bayes baseline, then explore LSTM, bidirectional RNN, CNN, tf hub transfer learning on 10 percent data.

build a baseline text classifier using tf-idf vectorization and a multinomial naive bayes model in a pipeline, then evaluate with accuracy, precision, recall, and f1.

Create a reusable evaluation function to compare binary classification models using accuracy, precision, recall, and weighted F1 score; apply it to the baseline and future neural network experiments in TensorFlow.

Builds and evaluates the first deep learning model for text data using a functional API, text vectorization, and learnable embeddings, with global average pooling producing a single disaster/not disaster prediction.

Learn how embeddings are learned and visualized with TensorFlow's projector tool, using a 128-d embedding per token for a ten thousand-word vocabulary, and observe clustering in reduced dimensions.

Explore how recurrent neural networks use previous inputs to shape current representations for sequence data like text, via embeddings and recurrent layers to model and predict outputs.

Build and train our first TensorFlow RNN with LSTM for text data, using tokenization and embedding, and evaluate a binary classification output.

Explore building and evaluating a GRU-based recurrent neural network in TensorFlow, including embedding, stacked GRU layers, return_sequences handling, and global average pooling for binary classification.

Build, train, and evaluate a bidirectional recurrent neural network in TensorFlow, comparing bidirectional LSTM/GRU with unidirectional models, using embedding, vectorization, and binary classification with cross-entropy.

Explore conv1d neural networks for text and sequences, turning tokenized text into embeddings, applying convolutional filters and max pooling to produce a final feature vector for classification.

Build a 1d cnn for text with embedding, 64 filters, kernel size 5, and max pooling, using a sigmoid output for binary classification; then explore transfer learning with TensorFlow Hub.

Explore transfer learning for NLP using TensorFlow Hub's universal sentence encoder to generate 512-dimensional embeddings, then build a dense output layer for text classification or semantic similarity tasks.

Learn to build a transfer learning model for NLP using TensorFlow Hub's universal sentence encoder as a fixed feature extractor, handling variable length text inputs with a dense binary classifier.

Apply transfer learning by adding a single dense layer to a pre trained model, achieving improved accuracy, and evaluate on a 10 percent data subset from Kagle.

Explore building, training, and evaluating a transfer learning model on 10% of the data by cloning a model, training with a small subset, and comparing performance to a full-data model.

Diagnose data leakage between training and validation splits that made model 7 outperform model 6 on ten percent data. Fix the splits, retrain model 7, and validate with transfer learning.

Compare eight modelling experiments and their evaluation metrics using pandas data frames, and visualize results to identify the best performer, highlighting a simple TF-IDF baseline.

Learn how to upload model training logs to TensorBoard.dev, visualize and compare architectures on the Kaggle tweets text classification dataset, using Colab and TensorBoard.dev commands.

Save and load a trained NLP model using H5 and saved model formats, load with custom objects, and verify performance with accuracy and F1 scores.

Download a pretrained model from Google storage and evaluate on validation data, visualizing the most wrong predictions in a binary classification using sigmoid probabilities to identify false positives and negatives.

Visualize false positives and false negatives in a binary disaster detector, inspecting top prediction examples, targets, and probabilities to understand model mistakes.

Visualize test data predictions and fix mislabeled samples to improve model accuracy in TensorFlow for deep learning bootcamp. Predict on random test tweets and classify as disaster or not disaster.

Explore the speed/score tradeoff in machine learning by comparing accuracy gains against time per prediction and deployment speed, using model six versus baseline on inference time and F1 score.