Certification in Deep Learning AI

Explore Python for deep learning, highlighting its readable syntax and libraries like NumPy, Pandas, TensorFlow, and PyTorch, plus data handling and visualization tools.

Explore NumPy for fast multidimensional array computations, Pandas for loading and cleaning structured data with data frames, and Matplotlib for visualizing trends with line, bar, and scatter plots.

Master scikit-learn basics for classification, regression, clustering, and dimensionality reduction, covering pre-processing, model training, evaluation, pipelines, and integration with NumPy and Pandas.

Master data preprocessing and feature engineering, covering data structures, NumPy arrays for tensors, and pandas file loading; build and tune neural networks with Keras.

Explore building and training deep neural networks with PyTorch, including a two-layer MLP for digit recognition, data preprocessing, cross-entropy loss, and Adam optimization, plus MNIST basics and project ideas.

Discover how Python powers deep learning with simple syntax and key libraries like NumPy and Pandas. Explore high-level frameworks such as TensorFlow and PyTorch for model building, training, and deployment.

Explore the fundamentals of neural networks, a model that learns patterns from data through layers of artificial neurons, weights, biases, activation functions, forward and back propagation, and loss.

Explore the parallels between biological neurons and artificial neurons, covering structure, learning via synaptic plasticity versus backpropagation, and how weights, bias, and activation functions drive neural networks.

Explore how perceptrons and multilayer perceptrons process inputs through weights and biases, use activation functions, and learn via backpropagation to model nonlinear relationships.

Explore activation functions in neural networks to introduce non-linearity and enable learning of complex patterns, including sigmoid, tanh, relu, softmax, and variants like leaky relu, impacting gradient behavior.

Understand forward propagation as data moves through layers with weights, biases, and activation functions to produce predictions. Then apply backward propagation to compute gradients and update weights via gradient descent.

Guide neural network learning by minimizing loss via gradient descent and backpropagation, using mean squared error, cross-entropy, and binary cross-entropy for regression and classification.

Discover how neural networks mimic the brain to learn patterns and make predictions via forward propagation, backpropagation, perceptrons, hidden layers, non-linearity, activation functions and optimization, and data preprocessing.

Explore deep neural networks with multiple hidden layers that learn complex patterns via forward and back propagation, activation functions, and gradient-based optimization across computer vision and natural language processing tasks.

Examine the architecture and hidden layers of deep neural networks from input to output, with activation functions and forward-backward training for image recognition, NLP, speech, and recommendations.

Learn how forward propagation, loss functions, and backward propagation drive neural learning, while gradient descent, SGD, Adam, and RMSProp optimize weights to minimize loss.

Explore overfitting and regularization techniques—L1, L2, dropout, early stopping, and data augmentation—to improve model generalization in deep learning.

Master weight initialization to stabilize forward and backward propagation and prevent vanishing or exploding gradients, with Xavier for sigmoid/tanh and HE for ReLU.

Explore batch normalization and dropout, learn how they stabilize training, reduce overfitting, and improve generalization across CNNs, RNNs, and transformers, with a Python Keras example and evaluation metrics.

Deep neural networks extend traditional models by stacking multiple hidden layers to automatically extract hierarchical features from raw data, powering computer vision, natural language processing, speech recognition, and predictive analytics.

Convolutional neural networks automatically learn spatial hierarchies from grid-like images using convolutional filters, pooling, and ReLU to perform image classification, object detection, and facial recognition.

Explore how convolution operations in cnns extract local features from images and signals by sliding a kernel, performing element-wise multiplications and sums to form feature maps that detect edges.

Downsample feature maps with pooling layers in convolutional neural networks using max or average pooling, reducing parameters and computations while preserving key features and translation invariance.

Examines CNN architectures from LeNet to ResNet, including AlexNet, VGG, and Inception, and demonstrates a simple CNN with conv, pooling, and dense layers in Keras.

Explore how convolutional neural networks classify images by learning features. Understand how pooling, activation, and softmax drive predictions, while augmentation and dropout boost generalization; evaluate with accuracy, F1, and ROC-AUC.

Explore how convolutional neural networks learn from images through convolution, pooling, and deep hierarchies to classify, detect objects, and analyze medical images.

Explore recurrent neural networks for sequential data, how LSTM gates preserve long-term memory to mitigate vanishing gradients, and an introduction to convolutional neural networks for image processing.

Explore sequence modeling basics that capture order and context with memory, using RNNs, LSTMs, and transformers for NLP, speech, time series, and translation.

Explore RNNs that process sequences with a hidden state, trained by backpropagation through time, highlighting weight sharing, the recurrence relation, vanishing gradients, and LSTMs and GRUs.

Explore sequence modeling that captures temporal patterns and context using RNNs and LSTMs, overcoming memory limitations and vanishing gradients to learn long-range dependencies in real-world tasks.

Learn to build, train, and preprocess LSTM models with TensorFlow or Keras, including sliding windows, min-max scaling, and exploring GRU, bidirectional, stacked LSTM, sequence-to-sequence, and attention.

Explore LSTM applications in sentiment analysis, text generation, predictive maintenance, and time series forecasting, plus NLP tasks like NER, translation, and language modeling.

Explore how recurrent neural networks and LSTMs model temporal patterns, overcome vanishing and exploding gradients with gated cell states, and power language processing, time series, and sequence-to-sequence tasks.

Explore transfer learning, where pre-trained models are fine-tuned for related tasks with less data, boosting accuracy in computer vision, NLP, and speech recognition.

Explore transfer learning, its domain mismatch challenges, including negative transfer when natural images differ from medical images, and the ethical and interpretability concerns, and resource demands.

Master feature extraction and fine-tuning in transfer learning, deciding when to freeze pre-trained layers or adapt deeper representations for new tasks. Learn when to use learning-rate strategies and progressive unfreezing to balance accuracy and stability.

Explore popular pre-trained models that accelerate transfer learning across computer vision and NLP, from VGG-16 and ResNet to BERT and GPT, plus the Hugging Face transformers ecosystem.

Explore hands-on transfer learning with pre-trained models, including ResNet-50 in Keras for flower classification and BERT via HuggingFace for sentiment analysis, with practices for fine-tuning at a low learning rate.

Transfer learning and pre-trained models accelerate deep learning by reusing knowledge via feature extraction. Fine-tuning further adapts to tasks with architectures like VGG, ResNet, MobileNet, BERT, GPT, and T5.

Explore generative deep learning with GANs and VAEs, models that learn data distributions and latent representations to synthesize realistic new samples for images, text, and audio.

Explore generative deep learning by contrasting discriminative and generative models, and learn how GANs use adversarial training between a generator and a discriminator to synthesize realistic data.

Explore generator and discriminator dynamics in GANs, mapping latent vectors to realistic outputs via transposed convolutions and activations, while stabilizing training with label smoothing, spectral normalization, and balanced learning rates.

Learn about dcgan, cgan, cyclegan, and stylegan, their design principles like batch normalization and adaptive instance normalization, and how they enable stable, conditioned, unpaired, and high-resolution image generation.

Explore variational autoencoders (VAEs) and their probabilistic latent space, encoder-decoder architecture, and the reparameterization trick, balancing reconstruction and KL divergence via ELBO for generation.

Explore variational autoencoders with TensorFlow and Keras on MNIST, covering the encoder outputs z-mean and z-log-var, the reparameterization trick, a 2D latent space, and generation, denoising, and interpolation.

Explore the diverse use cases of generative models, including image generation with GANs and VAEs, deep fakes, and data augmentation.

Explore hands-on variational autoencoders on MNIST, with a 2D latent space and reparameterization sampling. Build DCGANs for fashion-MNIST and assess results using IS and FID.

Explore generative deep learning, including GANs and VAEs, to learn data distributions and create realistic images, text, and audio for healthcare, NLP, and creative industries.

Explore how deep learning advances natural language processing to enable text classification, sentiment analysis, and named entity recognition. Build end-to-end neural pipelines for translation and chatbots.

Explore word embeddings like Word2Vec, GloVe, and FastText, and how dense vector representations capture semantic and syntactic relationships, context, and analogies for NLP tasks.

Explore sequence-to-sequence models built on RNNs, LSTMs, and GRUs, addressing vanishing gradients to capture long-term dependencies, and apply CNNs for NLP tasks with word embeddings and pooling.

Explore sequence-to-sequence models that map input sequences to output sequences with an encoder-decoder architecture, enabling machine translation, language generation, and question answering in NLP.

Explore transformers and the attention mechanism, including self-attention and long-range dependencies, enabling parallel processing for translation, summarization, and question answering. See how BERT, GPT, and T5 exemplify interpretable NLP.

Explore pre-trained language models like BERT and GPT, their transformer architectures, and fine-tuning for downstream NLP tasks, including sentiment analysis, NER, and text summarization.

Examine popular NLP datasets like IMDB, SQUAD, CONNL2003, TREC, GLUE, and WMT, and libraries such as Hugging Phase Transformers, SPACI, TensorFlow, Keras, PyTorch, and OpenNMT, outlining the end-to-end NLP workflow.

Explore how deep learning transforms NLP with word embeddings, RNNs, CNNs, seq-to-seq, attention, and transformers, and how BERT, GPT, and T5 enable transfer learning for sentiment analysis and machine translation.

Discover deep learning frameworks like TensorFlow and PyTorch, with automatic differentiation and GPU acceleration. Compare APIs such as Keras to low-level APIs and enable deployment across cloud, mobile, and edge.

Explore TensorFlow basics, from computation graphs and eager execution to tf.keras models, tensors, and data pipelines, plus a simple feedforward network using Adam and Cosentrophy for multi-class classification.

Explore PyTorch basics, featuring dynamic computation graphs, tensors, autograd, and nn.Module, then train with optim in a manual loop and visualize with TensorFlow and Matplotlib.

Explore PyTorch basics, including dynamic compute graphs, tensors, nn.Module, autograd, and optim, and learn a simple training loop with a 100-to-10 classifier, cross-entropy loss, and Adam optimizer.

Learn end-to-end image classification with TensorFlow, Keras, and PyTorch on CIFAR-10, featuring CNNs, training loops, normalization, TensorBoard visualization, and ONNX export and load.