Deep Learning with TensorFlow 2

Curriculum

• Introduction Free

  1 Lesson 7 Min
  Why machine learning Free
• Neural networks Intro Free

  12 Lessons 42 Min
  Introduction to neural networks Free Training the model theory Free Types of machine learning Free The linear model Free The linear model. Multiple inputs. Free The linear model. Multiple inputs and multiple outputs Free Graphical representation Free The objective function Free L2-norm loss Free Cross-entropy loss Free One-parameter gradient descent Free N-parameter gradient descent Free
• Setting up the environment

  6 Lessons 23 Min
  Setting up the environment - Do not skip, please! Why Python and why Jupyter Installing Anaconda Jupyter Dashboard - Part 1 Jupyter Dashboard - Part 2 Installing the TensorFlow package
• Minimal example

  4 Lessons 19 Min
  Outline Generating the data (optional) Initializing the variables Training the model
• Introduction to TensorFlow 2

  7 Lessons 23 Min
  TensorFlow Outline TensorFlow 2 Intro A note on coding in TensorFlow Types of file formats in Tensorflow and data handling Model layout - inputs, outputs, targets, weights, bias, optimizer, and loss Interpreting the result and extracting the weights and bias Customizing your model
• Deep nets overview

  8 Lessons 25 Min
  The layer What is a deep net Really understand deep nets Why do we need non-linearities Activation functions Softmax activation Backpropagation Backpropagation - intuition
• Backpropagation (optional)

  1 Lesson 0 Min
  Backpropagation mathematics
• Overfitting

  6 Lessons 20 Min
  Underfitting and overfitting. A regression example Underfitting and overfitting. A classification example Train vs validation Train vs validation vs test N-fold cross validation Early stopping - motivation and types
• Initialization

  3 Lessons 9 Min
  Initialization Types of simple initializations Xavier's initialization
• Optimizers

  7 Lessons 21 Min
  SGD&Batching Local minima pitfalls Momentum Learning rate schedules Learning rate schedules. A picture Adaptive learning schedules Adaptive moment estimation
• Preprocessing

  5 Lessons 15 Min
  Preprocessing Basic preprocessing Standardization Dealing with categorical data One hot vs binary
• Deeper example

  9 Lessons 36 Min
  MNIST dataset How to tackle the MNIST dataset MNIST - Importing libraries and data Preprocess the data - create a validation dataset and scale the data Preprocess the data - shuffle and batch Outline the model Select the loss and the optimizer Learning Testing the model
• Business case

  8 Lessons 39 Min
  Exploring the dataset and identifying predictors Outlining the business case solution Balancing a dataset Preprocessing the data Load the preprocessed data Learning and interpreting the result Setting an early stopping mechanism Testing the business model
• Conclusion

  6 Lessons 18 Min
  Summary What's more out there An overview of CNNs How DeepMind uses deep learning An overview of RNNs Non-NN approaches