The Complete Deep Learning Course 2024 With 7+ Real Projects

Discover pandas, the open source python library for data manipulation and analysis, using data frames and series to handle csv, excel, and sql data, with cleaning, aggregation, and merging.

Explore scikit-learn, an open source python library that provides a user-friendly suite of ml algorithms for supervised and unsupervised tasks, with preprocessing, cross-validation, and extensibility.

Explore matplotlib, a Python library, to create static, animated, and interactive visualizations for machine learning data, including exploratory data analysis, model evaluation, and decision boundary plots.

Seaborn is a Python data visualization library built on matplotlib that provides an interface for attractive statistical graphics, with built-in themes, color palettes, and plots that integrate with pandas.

Explore the basic concept of machine learning, including supervised, unsupervised, and reinforcement learning, with examples, algorithms, and steps from data collection to deployment.

Learn the three main machine learning types—supervised, unsupervised, and reinforcement learning—with examples of regression, classification, clustering, dimensionality reduction, and algorithms like SVM, neural networks, and Q-learning.

Explore logistic regression as a binary classification algorithm using the sigmoid function, probability estimates, and log loss, with applications, strengths, and limitations.

Explore how a decision tree, a supervised learning algorithm for classification and regression, splits data from the root node to leaf nodes using feature conditions to predict outcomes.

TensorFlow, an open source machine learning framework from Google Brain Team, supports tasks from linear regression to deep learning via an ecosystem featuring Keras integration, eager execution, and TensorFlow Lite.

Learn to handle missing values in data preprocessing with strategies like dropping, mean/median/mode imputation, forward/backward fill, kNN, regression, and advanced techniques such as multiple imputation.

Apply train test split to divide a data set into training and test sets, train on the training data, and evaluate on unseen test data to estimate model performance.

Scale and normalize features to a common range to improve distance-based model performance. Learn min-max scaling, standard (z-score) scaling, robust scaling, and L2/L1 normalization with scikit-learn examples.

Learn how to encode categorical variables for machine learning, converting labels to numbers and choosing methods like label, one-hot, binary, target, frequency, and hashing encodings for nominal and ordinal data.

Explore artificial neural networks, from neurons, layers, and activation functions to training and backpropagation, and see their applications in image recognition, speech processing, natural language processing, finance, and autonomous systems.

Understand activation functions and their non-linear role in neural networks, enabling learning of complex patterns. Learn about sigmoid, tanh, ReLU, leaky ReLU, and softmax for binary and multi-class tasks.

Explore how regularization prevents overfitting by penalizing model complexity with L1, L2, and elastic net, and apply dropout and early stopping to improve generalization.

Explore hyperparameter tuning, optimizing learning rate, batch size, epochs, and architecture to boost model performance.

Explore the perceptron as a simple neural network with inputs, weights, bias, and a step activation, trained on labeled data to classify, highlighting its linear separability limitation.

Explore the multilayer perceptron (MLP), the classic artificial neural network with input, hidden, and output layers, and learn activation functions, backpropagation, and gradient descent training for classification and regression tasks.

Kick off project 1 part 1 by mastering sentiment analysis, building a binary IMDb review classifier with TensorFlow and Keras, and preprocessing 50k texts for training and testing.

Build a neural text classifier with an embedding layer mapping review words to dense vectors, followed by global max pooling 1D, dense layers with dropout, and a sigmoid binary output.

Train the model end-to-end by loading data, building the view model, compiling with Adam and binary cross entropy, training, evaluating accuracy on test data, and predicting outputs.

Discover how convolutional neural networks process images using convolutional layers, ReLU activation, pooling, and fully connected layers for classification, with applications in image classification, object detection, segmentation, and video analysis.

Explore CIFAR-10, a 60,000-image 32×32 dataset with ten classes for benchmarking in image classification. Implement TensorFlow Keras model with 32 3×3 convolution filters, ReLU, max pooling, and dropout.

Implement a model with a flatten layer to transition between convolution and fully connected layers, a 512-unit ReLU dense layer, a 0.5 dropout, and a 10-class softmax output.

Train the model using training, validation, and test splits; compile with categorical cross entropy and an optimizer; monitor with TensorBoard; and evaluate with accuracy.

Improve the CIFAR ten model with a TensorFlow Keras convolutional network, including data loading and normalization. Train for 50 epochs and aim for 85% test accuracy using lecture resources.

Build and train a TensorFlow Keras convolutional neural network on cifar-10, with data loading, normalization, one-hot labels, and data augmentation. Use sequential blocks, batch normalization, pooling, dropout, and softmax output.

Learn how data augmentation broadens training data by applying rotations, flips, scaling, translations, brightness and noise adjustments, plus text and audio augmentations, to improve generalization and robustness in image classification.

Learn to build a recurrent neural network for sequential data analysis using an Elman network with two layers, training on cosine-based waveforms and plotting training and predictions.

LeNet is an early convolutional neural network by Yann LeCun designed for handwritten digit recognition, using convolutional and pooling layers followed by fully connected and softmax output.

Learn how residual networks enable training of very deep models using shortcut connections and residual learning, with bottleneck architectures across ResNet-18 to ResNet-152.

Explore Inception v3, a Google developed deep convolutional neural network for high-accuracy image classification with computational efficiency, featuring Inception modules, factorized convolutions, auxiliary classifiers, and batch normalization.

Explore alexnet, a pioneering convolutional neural network that advanced deep learning and computer vision with deep convolutional layers, ReLU activation, local response normalization, pooling, dropout, and data augmentation.

Explore highway networks and dense nets, detailing gating mechanisms with transform and carry gates for deep network training, and dense connections with feature reuse through concatenation.

Leveraging depthwise separable convolutions, Xception reduces parameters and computation, and organizes its layers into entry, middle, and exit flows for efficient image classification.

Explore sequential data analysis by examining data ordered in sequence, noting order dependency, temporal information, and patterns that reveal trends in time series, text, event, and genetic data.

Build a recurrent neural network for sequential data analysis by generating cosine waveforms with varying amplitudes and train an Elman network, then plot training results and ground truth versus predictions.

Learn how transfer learning reuses pre-trained models to solve related tasks, through fine-tuning or feature extraction, reducing training time and boosting performance across computer vision, NLP, and speech recognition.

OpenCV is an open source computer vision library for image and video processing, feature and object detection, and real-time applications, with CUDA acceleration and TensorFlow or PyTorch integration.

Explore YOLO, or you only look once, a single-pass real-time detector using grid-based prediction to estimate bounding boxes, confidence scores, and class probabilities with non-maximum suppression for fast, global understanding.

Explore Neurolab, a simple and flexible Python library for building and training neural networks. Create feedforward, recurrent, and radial basis function networks with a simple API and multiple training algorithms.

Explore forward propagation in RNNs, where the network processes input sequences one time step at a time, updating a hidden state to remember past context and generate outputs.

Explain back propagation through time in RNNs, detailing how it computes gradients, unrolls the network, updates parameters with gradient descent or Adam, and addresses vanishing and exploding gradients.

Visualize the characters by loading the input data in a Colab notebook, importing cv2 and numpy, reshaping and resizing the data, and displaying each character with cv2.

Object detection identifies and locates objects in images with bounding boxes. Use YOLO, faster R-cnn, SSD, and Retinanet for fast, accurate detection in autonomous vehicles, surveillance, and augmented reality.

Implement a YOLO object detector in Colab by importing the YOLO module, loading a pretrained model, predicting on an image, and visualizing bounding boxes with class labels.

Implement a deep neural network with two hidden layers of ten neurons using neural lab, train with gradient descent on generated data, and compare ground truth versus predicted output.

Explore gradients with respect to hidden-to-hidden weights in RNNs and how backpropagation through time updates w_hh to preserve memory, addressing vanishing and exploding gradients with gradient clipping and LSTM/GRU.

Explore how the input-to-hidden weight matrix U connects x_t to h_t in an RNN, and how backpropagation through time computes and updates gradient of the loss with respect to U.

Explore how the RNN hidden-to-output weight matrix V maps hidden states to outputs and how the loss gradient with respect to V guides updates to reduce error.

Master gradient clipping to stabilize neural network training and prevent exploding gradients in deep networks and RNN, using clipping by value or clipping by norm to improve convergence and performance.

Explore vanishing and exploding gradient problems during backpropagation in deep networks and RNNs. Learn solutions like ReLU, Xavier or He initializations, batch normalization, gradient clipping, and LSTM/GRU architectures.

Implement a song generation project using nets in TensorFlow 1.1.0 within Colab notebooks, loading song data.csv, building a character-level RNN with one-hot encoding and character mappings.

Define project 3 neural network parameters: hidden size 100, input/output sequence length 25, and seed 42 for reproducibility. Initialize placeholders and weights, and outline forward propagation for recurrent neural network.

Implement a feedforward network across time steps, defining input, hidden, and output weights and biases, computing y_hat, applying softmax, and setting up cross-entropy loss with adam optimizer and gradient clipping.

Initialize the Adam optimizer, compute the loss gradient, and clip gradients by value with a 5.0 threshold using tf.constant to stabilize training and prevent exploding gradients.

Build and train a character-level rnn for song creation using tensor flow, initialize variables, slide a 25-character input window, one-hot encode inputs and targets, and generate predictions every 500 iterations.

Continue the final part of the song creation project by storing predicted character indices, converting inputs to one-hot vectors, computing next-character probabilities, and generating output through a TensorFlow session.

Outline course structure of the complete deep learning course, covering activation functions, CNN projects, sentiment analysis, NLP, handwritten digit recognition, restricted Boltzmann machines, and stock price prediction with reinforcement learning.

Watch all video content to follow step-by-step solutions and gain full explanations. Engage with the Q&A, ask questions, help others, and stay curious to maximize learning and enjoyment.

Explore what a neuron is and how neurons in the brain work as units. See how they connect via a synopsis, receive input through dendrites, and send motor commands.

Deep learning is the modern name for artificial neural networks with many layers. These networks learn intricate data patterns through layered structures, enabled by computational advances, advancing machine learning.

Explore how artificial neural networks use layered neurons—input, hidden, and output layers—to learn data representations, extract features, and handle binary, multiclass, or regression tasks.

Explore how TensorFlow, Google's open source library for numerical computation and deep learning, enables scalable execution across Windows, Linux, and Android, with dataflow graphs and visualization of multi-dimensional arrays.

Discover the course tools, focusing on Google Colab for easy setup and data access. If you prefer, download the code as Jupyter notebooks, noting data download locations vary.

Explore multilayer neural networks and how hierarchical layers share information to extract robust features and invariances, and compare convolutional neural networks, recurrent networks, deep belief networks, and restricted boltzmann machines.

Discover how Pandas simplifies data analysis with data frames, loading tabular data, and easy data manipulation. Visualize relationships in the iris dataset using scatter plots, histograms, and box plots.

Learn how to encode categorical variables with one-hot encoding using pandas get_dummies and handle missing values before feeding data into machine learning models.

Download and install Anaconda, choose a location, decide whether to add environment variables, set Anaconda as the default entry point, and be prepared for a lengthy installation.

Master neural network basics by examining Adam optimizer, loss functions such as mean squared error, forward and backward propagation, training metrics, and true/false positive/negative evaluations.

Explore how activation functions introduce non-linearity in neural networks to enable learning of complex patterns. Recognize why omitting activation makes neurons behave like linear regression and preview common activation functions.

Explore the sigmoid activation function, a differentiable logistic function in a neural network that maps inputs to a value between zero and one and predicts output probability.

Explore the tanh function and its expression using exponentials. See how it centers at zero and remains differentiable and monotonic, like a sigmoid.

Explore the leaky rectified linear unit variant to address the dying ReLU problem, showing how negative inputs are scaled by a small alpha, typically about 0.01.

Explain the leaky ReLU function, a variant of ReLU with a small negative slope, typically with alpha around 0.01.

Explore the exponential function and the exponential linear unit, showing how it curves for negative inputs and follows a linear path for non-negative inputs, as shown in the diagram.

Discover the swish activation function, a parameterized, non-monotonic activation that can interpolate between linear and nonlinear behavior; beta zero yields the identity function, offering potentially better performance than relu.

Explore how the softmax function converts network outputs into class probabilities that sum to one for multiclass classification, and compare it with sigmoid using the exp(z_j)/sum_k exp(z_k) formula.

Implement activation functions in code, including sigmoid, tanh, relu, leaky relu, elu, swish, and softmax. Apply these concepts to train and improve models in AI for health care.

Explore a prediction project that uses a multilayer neural network to predict compressive strength, a nonlinear function of age and ingredients, avoiding destructive testing.

Import data and libraries in Python to build a concrete compressive strength model from mixture ingredients, using a quantitative dataset with pandas in Google Colab.

Perform exploratory analysis on a real dataset by computing descriptive statistics to reveal central tendency, dispersion, and distribution for numeric and object columns.

Explore data visualization with seaborn to reveal distributions, relationships, and correlations using box plots, scatter plots, and pair plots; compare variables and assess target and predictors with linear regression visuals.

Scale features to a common range, typically zero to one, to improve predictive accuracy and allow fair comparisons across variables with different distributions and units, using the preprocessing package.

This lecture demonstrates building and training a deep neural network to predict concrete quality from ingredients, covering data split, scaling, a four-layer dense model, and evaluation with R^2.

Evaluate the model with the coefficient of determination, a measure of how well it predicts variance, with zero to one values, where one is best and negative scores can occur.

Identify and remove outliers using the turkey method to reduce distortion in descriptive statistics and correlation, then retrain the model to raise the R² from about 86-87% to 91%.

The project covers data preparation, scaling, and training, shows outlier removal boosting accuracy from 7% to 92%, and uses neural networks to predict compressive strength from mixture ingredients.

Demonstrate convolutional neural networks inspired by the visual cortex, using convolution, receptive fields, pooling, and fully connected layers to process and classify images.

Learn how convolutional layers in a CNN apply filters across the input volume to produce two-dimensional activation maps, using local receptive fields and weight sharing to capture diverse features.

Explore pooling layers in convolutional networks, including max pooling and average pooling. Understand how window size and stride reduce spatial dimensions, lowering parameters and computation, and improving invariance to position.

Explore a sci-fi second project and build a CNN model to recognize images collected by Canadian researchers. Prepare for mobile deployment as you begin this engaging image recognition project.

Load libraries and data to set up a convolutional neural network; split into train and test and divide by 255.

Compile and train a cnn in TensorFlow by importing layers, adding convolutional layers with relu, pooling, dropout, normalization, flatten, dense layers, and an optimizer.

Train a neural network by feeding CNN data, using early stopping and learning-rate reduction to curb overfitting and optimize validation loss.

Analyze the numerical results of the model, presenting a classification report and confusion matrix, with class accuracies from 64% to 87% and an overall around 80%.

Learn to visualize the first convolutional layer with 64 filters to see what a cnn learns from color images. Note that deeper networks are harder to interpret.

Explore the project's end-to-end process from data preparation and labeling to training a convolutional neural network with one- and two-dimensional convolutions, pooling, learning filters, and visualizing results.

Learn how convolutional neural networks classify images by extracting low-level features via trainable convolutional filters and relu activations, then pooling and flattening for downstream classification.

Import libraries and load a fashion image dataset with 60,000 training and 10,000 testing grayscale images across 10 classes, then map labels to items like ankle boots and trousers.

Visualize radar data by scaling image values from 0–255 to 0–1, arrange multi-panel displays, and preview class names during neural network training for deep learning projects.

Design and build a convolutional neural network model with a feature extractor of convolutional and pooling layers and a classifier, including reshaping grayscale images for four-dimensional input and end-to-end training.

Train a model for ten iterations, evaluate with test data, and visualize predicted probabilities against true labels to achieve about 98.5 percent accuracy and refine plotting.

Visualize the convolutional filters from a layer with 64 filters arranged in four rows and four columns, and explore data argumentation as a solution for limited samples.

Boost the accuracy of a small clothing image classifier with data augmentation using a CNN and a generator, achieving 79.5% to 80.6% accuracy while avoiding flips that distort clothing images.

The project trains a cnn with three layers to classify fashion and clothing images, uses grayscale preprocessing, data augmentation, and iterative training to raise accuracy from 79.5% to 86%.