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 neural networks with TensorFlow and Keras, understanding forward and backward propagation and activation functions. Apply these concepts to image classification and other real-world tasks.

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 with TensorFlow and Keras to classify images, using convolutional, pooling, and fully connected layers with ReLU and softmax activations, dropout, and data augmentation.

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.

Apply transfer learning with TensorFlow to fine-tune pre-trained models from ImageNet for new tasks. Learn to freeze base layers, add new top layers, and fine-tune for better animal image classification.

Explore natural language processing with TensorFlow to build text classification and sentiment analysis models. Process text data, apply pre-trained embeddings, and implement an LSTM-based classifier to identify topics and sentiment.

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.

Master time series forecasting with TensorFlow by preprocessing data, creating time windows, and building models, including dense networks and LSTM, to predict future values from historical data.

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.

deploy and serve a trained TensorFlow model in production using TensorFlow serving or a Flask API, packaging for cloud deployment on AWS, Google Cloud, or Azure, with monitoring.

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.