Discover the tools software developers use to build scalable AI-powered algorithms in TensorFlow, a popular open-source machine learning framework.
In this four-course Specialization, you’ll explore exciting opportunities for AI applications. Begin by developing an understanding of how to build and train neural networks. Improve a network’s performance using convolutions as you train it to identify real-world images. You’ll teach machines to understand, analyze, and respond to human speech with natural language processing systems. Learn to process text, represent sentences as vectors, and input data to a neural network. You’ll even train an AI to create original poetry! - Source

• Huber Loss: https://en.wikipedia.org/wiki/Huber_loss

• Week 1
  • Lesson Topic: Introduction to ML, Introduction to TensorFlow
  • Quiz: Basic ML and TensorFlow
  • Optional: Housing prices
• Week 2
  • Lesson Topic: Introduction to Computer vision, Data processing, MNIST data, Neural Network, Callback function implementation
  • Quiz: Data processing, callbacks
  • Optional: Handwriting recognition
• Week 3
  • Lesson Topic: Convolutions, Pooling, ConvNet, Filters, Padding
  • Quiz: Convolution Neural Network
  • Optional: MNIST with convolutions
• Week 4
  • Lesson Topic: ImageGenerator, Designing of neural network, ConvNet train, Impact of compressing images
  • Quiz: Image Generator
  • Optional: Handling complex images

• Week 1
  • Lesson Topic: Cats vs Dogs datasets, Visualizing the effect of the convolutions, accuracy and loss
  • Quiz: Convolutions
  • Optional: Cats Vs Dogs
• Week 2
  • Lesson Topic: Image Augmemtation, Overfitting
  • Quiz: Image Augmentation
  • Optional: Cats vs Dogs using augmemtation
• Week 3
  • Lesson Topic: Transfer learning, Dropouts
  • Quiz: Transfer learning and Dropouts
  • Optional: Horses vs. humans using Transfer Learning
• Week 4
  • Lesson Topic: Multiclass Classifications
  • Quiz: Multiclass classifications
  • Optional: Multi-class classifier

• Week 1
  • Lesson Topic: Tokenizer (tf.keras.preprocessing.text), pad_sequence (tf.keras.preprocessing.sequence)
  • Quiz: Tokenizer
• Week 2
  • Lesson Topic: IMDB Datasets, Tesorflow Datasets, Subwords Text Encoder
  • Quiz: IMDB Datasets
  • Optional: BBC news archive
• Week 3
  • Lesson Topic: Sequence Modeling, LSTM, Accuracy and Loss, Convolutional Network
  • Quiz: LSTM, GRU, Conv1D and NLP
  • Optional: Single Layer LSTM, Multi Layer LSTM, 1D Convolutional Layer, Bidirectional LSTM, GRU, Exploring overfitting in NLP
• Week 4
  • Lesson Topic: Sequence Models and Literature
  • Quiz: Tokenizer, Conv1D, Datasets, Padding, Prediction, LSTM
  • Optional: Shakespeare

• Week 1
  • Lesson Topic: Introduction of time series, Errors, MSE, RMSE, MAE, MAPE, Forecasting
  • Quiz: Different Types of Time Series with Example
  • Optional: Create and predict synthetic data
• Week 2
  • Lesson Topic: Preparing Features and Labels, Sequence Bias, Feeding Windowed Dataset, Single Layer NN, Time Windows, Prediction, DNN Training-Tuning-Prediction, DNN
  • Quiz: Time Windows, Types of Error, Callbacks, Learning Rate
  • Optional: Predict with a DNN
• Week 3
  • Lesson Topic: RNN, Lambda Layers, Huber Loss, LSTM
  • Quiz: RNN, Loss, LSTM
  • Optional: Mean Absolute Error
• Week 4
  • Lesson Topic: Convolutions, Bi-directional LSTMs, Batch Size, Real Data with Train-Tune-Prediction
  • Quiz: Convolution, CSV File, Error, Time Series
  • Optional: Sunspots