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Start
Introduction
What is Tensorflow
Start of Coding
Importing Tensorflow into a Notebook
Building a Deep Neural Network with Fully Connected Layers
Training/Fitting a Tensorflow Network
Making Predictions with Tensorflow
Calculating Accuracy from Tensorflow Predictions
Saving Tensorflow Models
Loading Tensorflow Models
Module 1: Machine Learning Fundamentals
Module 2: Introduction to TensorFlow
Module 3: Core Learning Algorithms
Module 4: Neural Networks with TensorFlow
Module 5: Deep Computer Vision - Convolutional Neural Networks
Module 6: Natural Language Processing with RNNs
Module 7: Reinforcement Learning with Q-Learning
Module 8: Conclusion and Next Steps
Intro/hello/how to approach this video
[Keynote] 1. What is deep learning?
[Keynote] 4. What is deep learning actually used for?
[Keynote] 7. What we're going to cover
10. Creating tensors with tf Variable
12. Shuffling the order of tensors
16. Manipulating tensors with basic operations
19. Matrix multiplication part 3
22. Tensor troubleshooting
25. One-hot encoding tensors
27. Using TensorFlow with NumPy
[Keynote] 29. Inputs and outputs of a regression model
32. Steps in modelling with TensorFlow
35. Steps in improving a model part 3
38. Evaluating a model part 3 (model summary)
40. Evaluating a model part 5 (visualizing predictions)
43. Evaluating a regression model part 8 (MSE)
46. Comparing and tracking experiments
49. Saving and downloading files from Google Colab
52. Putting together what we've learned 3 (improving our regression model)
[Code] 54. Preprocessing data 2 (normalizing data)
[Keynote] 56. Introduction to neural network classification with TensorFlow
[Keynote] 59. Typical architecture of a classification model
62. Building a not very good classification model
65. Making our poor classification model work for a regression dataset