Practical NLP & DL: From Text to Neural Networks (12+ Hours)

Learn NLP workflows from tokenization and stemming to text-to-vector methods like bag of words and word2vec, plus deep learning basics and a bidirectional LSTM sentiment project.

Explore practical use cases of NLP, from improving search engines with tokenization, embeddings, and transformers like Bert, to extracting insights from medical notes for healthcare and detecting fraud in finance.

We compare NLTK and SpaCy to highlight design, functionality, and use-case differences, showing NLTK's research and education focus and SpaCy's production-ready, real-world optimization.

Tokenization splits raw text into tokens—words or characters—that become the building blocks for NLP, and includes methods like word_tokenize, word_tokenize with punctuation, and TreeBank word tokenizer.

Explore how stemming reduces words to root forms and lemmas with eating to eat and happiness to happy, and compare porter and regex stemmers in NLTK.

Explore the snowball stemmer and its higher accuracy on large datasets, showing how to import and apply it with language English to stem words.

Explore lemmatization and compare it with stemming, highlighting how lemmatization returns dictionary forms using grammar rules and part of speech awareness for accurate NLP tasks such as chatbot sentiment analysis.

Explore stopwords and text preprocessing in NLP, showing how removing common words reduces data size, speeds up processing, and can improve sentiment analysis accuracy with a practical Python example.

Explore parts of speech and POS tagging, and learn how words are classified as verbs, adjectives, or nouns, and how tagging supports named entity recognition and grammar tools.

Learn named entity recognition (ner) and how it detects persons, organizations, places, money, and dates, with tokenization and tagging in a Python VS Code workflow.

Explore text pre-processing methods, from tokenization and lowercasing to regular expressions and stopwords, and learn vectorization techniques like one-hot encoding, bag of words, tf-idf, and word2vec, emphasizing data set relevance.

Explore text vectors by equating words to ingredients and converting them into precise measurements, enabling machines to grasp sentence meaning and powering search engines, chatbots, and spam filters.

Explore hot encoding versus label encoding through pizza analogy, learn representations, and apply pandas get_dummies and sklearn's onehotencoder, noting advantages and drawbacks like sparse matrices and out of vocabulary terms.

Understand bag of words as a text pre-processing method that tokenizes text, builds a vocabulary, and counts word frequencies to form vectors, noting fixed-size input and order ignored.

Explore n-grams—unigram, bigram, and trigram—to preserve context and word order in text vectorization. See how to apply sklearn's CountVectorizer with ngram_range and compare to bag-of-words, noting limitations.

Explore tf-idf, a statistical measure using term frequency and inverse document frequency to convert documents into weighted vectors, with formulas and a practical sklearn implementation.

Explore word embeddings and word2vec, comparing dense vector representations to sparse methods like bag of words and tf-idf. Learn cbow and skip-gram, semantic analogies, and how PCA reduces dimensions.

Explore artificial neural networks from neurons and weights in input, hidden, and output layers to activation functions, loss function, and optimizers like gradient descent, SGD, and Adam to improve predictions.

Explore cbow and skip-gram models for word2vec, predicting target from context and context from target, with a hands-on Python walkthrough using Gensim to train and tokenize text.

Learn how average word2vec creates a sentence vector by averaging word vectors, preserving semantic information for variable length text, using Python and cosine similarity to compare CBOW and skip-gram models.

Explore the evolution of big data and AI from the 1990s data explosion to deep learning breakthroughs, GPUs, Spark, and the rise of transformers and generative AI.

explore the advantages and disadvantages of the perceptron, including its simplicity, binary classification focus, and non-linear limitations, then learn about single-layer and multilayer networks with backpropagation and activation functions.

Explore multilayer perceptron basics through a mathematical walkthrough, building a neural network with inputs x1, x2, x3, weights, biases, sigmoid activation, and back propagation for loss and cost.

Learn backpropagation in a neural network by updating weights with the learning rate and loss, and compare regression losses (mse, mae, huber) with classification losses (binary and categorical cross entropy).

Apply the chain rule of derivatives to backpropagation, deriving the weight update formula, and examine the vanishing gradient problem in deep nets and its mitigation by activation functions.

Witness a practical demonstration of the sigmoid activation function and its derivative, and explore how vanishing gradients arise in a ten-layer network during backward passes.

Explore the tanh activation function, its implementation and applications in deep learning, detailing its -1 to 1 range, zero-centering, derivative is 1 - tanh^2(x), and its advantages over sigmoid.

Explore the ReLU activation function, defined as max(0, x), its property of avoiding vanishing gradients, faster convergence, and a numpy-based implementation with derivative.

explains leaky ReLU and parametric ReLU as improvements to ReLU, addressing the dead neuron problem with a small negative slope and a learnable alpha during training.

Explore the elu activation function, its exponential linear unit form and derivative for backpropagation, and compare benefits like no dead neurons and zero-centering with its higher computational cost.

Explore softmax activation for multiclass classification, turning logits into probabilities via exponentiation and normalization, with numerical stability tricks and a Python numpy implementation.

Compare activation functions across multi-class, multi-label, and binary classifications, noting softmax for multi-class and sigmoid for others; discuss ReLU and leaky ReLU for hidden layers and exploding gradients in RNN/LSTM.

Explore how regression models quantify errors, compare loss and cost functions, and apply mean squared error, mean absolute error, Huber loss, and RMSE to update weights.

Explore entropy in deep learning, from Shannon entropy to cross-entropy losses in classification. Learn how low entropy signals confidence and high entropy signals uncertainty, with binary, categorical, and sparse variants.

Recap hidden and output layer configurations: ReLU with sigmoid for binary classification (binary cross entropy), softmax for multi-class (categorical or sparse cross entropy), and linear with MSE for regression.

Visualize vanishing and exploding gradients, compare RNNs and CNNs with their time steps and global clipping, and note transformers, ChatGPT, layer normalization, and initialization.

Explore gradient descent optimizers, including mean squared error and the weight update rule, with epochs, batches, initialization, and the per-epoch workflow, ending with SGD.

Explore SGD, the core optimization algorithm that updates weights via the loss gradient. Compare vanilla SGD with mini-batch and momentum, and note learning rates.

Explore Adagrad, an adaptive gradient descent optimizer that scales learning rates per parameter based on historical gradients, enabling sparse NLP data handling and improved learning versus SGD.

Explore rmsprop and adadelta in practical NLP and DL, showing how rmsprop adapts learning rates per parameter for sparse data and vanishing gradients, while adadelta requires no learning rate.

Learn how the Adam optimizer blends SGD with momentum and RMSProp into an adaptive, first-order gradient method, offering robustness, per-parameter learning rates, and cross-architecture applicability.

Explain the exploding gradient problem, how gradients grow exponentially during backpropagation in deep networks, causing unstable updates and NaN values, and contrast with vanishing gradients, with solutions like gradient clipping.

Explore weight initialization techniques and their impact on forward and backward propagation in neural networks. Learn uniform, Xavier, and He initializations and how they control variance and exploding gradients.

Explore dropout layers as a powerful regularization method that randomly deactivates neurons during training to prevent overfitting, boosting robustness and efficiency, especially in large networks with limited data.

Compare ANN, CNN, and RNN, detailing data types for each, and explain CNN's convolution and pooling, RNN's recurrent layers, and their strengths in spatial and sequential tasks.

Discover convolutional neural networks and their feature detectors, kernels, pooling, and weight sharing, enabling visual understanding, mnist digits recognition, and scalable object detection with TensorFlow.

Explore how images are built from pixels, the language of pixels, with RGB and grayscale forming 4x4 and 4x4x3 representations, featuring 8 million pixels and 0–255 color values in 4k.

Apply a small convolution filter to a six-by-six grayscale image to generate a four-by-four feature map, and understand how padding resolves information loss in convolutional neural networks.

Explore how padding in convolutional neural networks preserves edge information, comparing valid and same (zero padding) methods, with a TensorFlow demonstration on grayscale images.

Explore how convolutional neural networks compute feature maps via padding and kernels, then detect edges, textures, and objects through early to late layer processing.

Explore max pooling, mean pooling, and minimum pooling in neural networks, their roles in dimensionality reduction and translation invariance, and implement all three with practical TensorFlow examples on CIFAR-10.

Learn the mNIST workflow from grayscale 28x28 images to a cnn classifier. Explore conv layers, 5x5 filters, max pooling, relu, flattening, and softmax for digit recognition.

end-to-end mnist cnn implementation in tensorflow, covering data loading, preprocessing, model building with convolutional layers, max pooling, flattening, dense layers, softmax output, compilation, training, evaluation, and visualization.

Use early stopping as a callback to halt training when the model stops improving. Monitor loss, set patience, and enable restore_best_weights to prevent overfitting and save compute.

Recaps CNN fundamentals, including convolution, padding, and pooling, and a practical end-to-end MNIST demo; previews advanced architectures and applications for future study, and signals the shift to RNN for NLP.

Explore natural language processing basics, from text data and vector representations to key tasks like text classification, sentiment analysis, machine translation, and speech recognition.

Explore the simple RNN architecture and how memory handles sequential data and time series, then cover backpropagation through time and gradient challenges. Also note solutions like LSTM and gradient clipping.

Implement a simple RNN in Keras using a sequential model with 50 units to learn a one-feature time series of 3 timesteps, train with Adam, and evaluate with MSE loss.

Explore forward propagation in a simple rnn, updating hidden states with x_t and h_{t-1}, using one-hot encoding and tanh activation to compute outputs with shared weights and biases.

Explore backward propagation in simple RNNs, showing gradients flowing from right to left through time and how eta-driven weight updates follow from gradient flow equations.

Identify the problems of simple RNNs: vanishing gradients, exploding gradients, and weak short-term memory. Explore LSTMs, GRUs, and transformers with self-attention and parallel processing for text and translation.

Explore how lstm architectures overcome rnn limitations by using a long term memory conveyor belt, four neural network layers, and gates to regulate information flow for context in sequences.

Explore how the forget gate in LSTMs selectively discards or retains information based on the previous hidden state and current input, enabling learning of long-term dependencies.

Explore how the LSTM input gate filters new information into candidate memory using the current input and previous hidden state, with sigmoid and tanh activations.

The lecture details how the LSTM output gate controls memory exposure, using sigmoid and tanh to produce context-aware predictions from the current input and previous hidden state.

Explore lstm variations, including peephole connections and coupled forget and input gates. Compare gru (gated recurrent unit), where update and reset gates reduce parameters.

Explore BiRNNs for sequence modeling, reading text left-to-right and right-to-left to capture past and future context, with applications in entity recognition, sentiment analysis, and translation.

Build a sentiment analysis project with bidirectional LSTM on IMDb data, train in a Jupyter notebook, save as a pickle file, and deploy locally with Streamlit.

Compare and explain an updated sentiment analysis model with dropout and bidirectional LSTM, highlighting batch size, epochs, and improved training convergence on the IMDb dataset.