README.md

Hands-on Deep Learning: TensorFlow Coding Sessions

This repository has the code for the Hands-on Deep Learning: TensorFlow Coding Sessions. The videos will be uploaded on a weekly basis.

The series consist of the introductory TensorFlow tutorials outlined below:

#	Tutorial	Code	Video
1	Introduction to TensorFlow: graphs, sessions, constants, and variables	S1 and S1_notebook.ipynb	Video #1
2	Training a multilayer perceptron	S2_live.py	Video #2
3	Setting up the training and validation pipeline	S3_live.py	Video #3
4	Regularization, saving and resuming from checkpoints, and TensorBoard	S4_live.py	Video #4
5	Convolutional neural networks, batchnorm, learning rate schedules, optimizers	S5_live.py	Video #5
6	Converting a dataset into TFRecords, training an image classifier, and freezing the model for deployment	S6	Video #6
7	Transfer learning: fine tuning a model in TensorFlow	S7	Video #7
8	Using a Python iterator as a data generator and training a denoising autoencoder	S8	N/A
9	What is new in TensorFlow 2.0 [new]	S9	Video #8

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Deep Learning Crash Course

A series of mini-lectures on the fundamentals of machine learning, with a focus on neural networks and deep learning.

• Lecture #1: Introduction
• Lecture #2: Artificial Neural Networks Demystified
• Lecture #3: Artificial Neural Networks: Going Deeper
• Lecture #4: Overfitting, Underfitting, and Model Capacity
• Lecture #5: Regularization
• Lecture #6: Data Collection and Preprocessing
• Lecture #7: Convolutional Neural Networks Explained
• Lecture #8: How to Design a Convolutional Neural Network
• Lecture #9: Transfer Learning
• Lecture #10: Optimization Tricks: momentum, batch-norm, and more
• Lecture #11: Recurrent Neural Networks
• Lecture #12: Deep Unsupervised Learning
• Lecture #13: Generative Adversarial Networks
• Lecture #14: Practical Methodology in Deep Learning

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