TensorFlow for Deep Learning: Build AI Models Like a Pro

Explore supervised learning with TensorFlow, building linear regression and logistic regression models, and apply gradient descent optimization with loss functions for regression and classification tasks.

This hands-on TensorFlow lecture builds a linear regression model y = mx + b, trained with mean squared error. Final parameters: m ≈ 1.99 and b ≈ 6.02 via SGD.

Train a logistic regression model in TensorFlow for binary classification using sigmoid output and binary cross entropy loss, updating weights m and bias b through gradient descent.

Build and train a neural network with TensorFlow and the Keras API to classify handwritten digits from the mNIST dataset, applying backpropagation, gradient descent, and dropout to prevent overfitting.

Build and train a CNN in TensorFlow to classify handwritten digits from mNIST using convolutional layers, pooling, and dropout; optimize with Adam and sparse cross-entropy and evaluate on test data.

build a sentiment analysis model for movie reviews with TensorFlow and Keras, using IMDb data, tokenization, padding, an LSTM, and sigmoid output; evaluate and predict.

Learn how sequence-to-sequence models in TensorFlow transform one sequence into another, using encoder-decoder architectures with LSTM or GRU and attention for machine translation, text generation, and summarization.

Build a time-series stock price predictor in TensorFlow using an LSTM trained on historical data. Learn data collection with yfinance, preprocessing with min-max scaling, windowing, training, evaluation, visualization, and forecasts.

Explore advanced deep learning with TensorFlow, covering cnn, rnn, and transformer architectures, transfer learning, distributed training, and production-ready optimization techniques like quantization and pruning.