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

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.

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.

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.

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 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 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.

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 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.

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.

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.

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.

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.

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.