Section 1 : Welcome
Lecture 1 Introduction and Outline copy 00:02:42 Duration
Lecture 2 Where to get the code 00:08:27 Duration
Lecture 3 Anyone Can Succeed in this Course 00:11:55 Duration

Section 2 : Google Colab
Lecture 1 Intro to Google Colab, how to use a GPU or TPU for free
Lecture 2 Uploading your own data to Google Colab
Lecture 3 Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn 00:08:54 Duration

Section 3 : Machine Learning and Neurons
Lecture 1 Review Section Introduction 00:02:38 Duration
Lecture 2 What is Machine Learning 00:14:26 Duration
Lecture 3 Code Preparation (Classification Theory) 00:15:59 Duration
Lecture 4 Beginner's Code Preamble 00:04:38 Duration
Lecture 5 Classification Notebook 00:08:40 Duration
Lecture 6 Code Preparation (Regression Theory) 00:07:19 Duration
Lecture 7 Regression Notebook 00:10:35 Duration
Lecture 8 The Neuron
Lecture 9 How does a model learn 00:10:54 Duration
Lecture 10 Making Predictions 00:06:45 Duration
Lecture 11 Saving and Loading a Model 00:04:28 Duration
Lecture 12 Suggestion Box 00:03:04 Duration

Section 4 : Feedforward Artificial Neural Networks
Lecture 1 Artificial Neural Networks Section Introduction 00:06:00 Duration
Lecture 2 Forward Propagation 00:09:40 Duration
Lecture 3 The Geometrical Picture 00:09:44 Duration
Lecture 4 Activation Functions 00:17:18 Duration
Lecture 5 Multiclass Classification 00:08:41 Duration
Lecture 6 How to Represent Images 00:12:37 Duration
Lecture 7 Code Preparation (ANN) 00:12:42 Duration
Lecture 8 ANN for Image Classification 00:08:37 Duration
Lecture 9 ANN for Regression 00:11:05 Duration

Section 5 : Convolutional Neural Networks
Lecture 1 What is Convolution (part 1) 00:16:38 Duration
Lecture 2 What is Convolution (part 2) 00:05:57 Duration
Lecture 3 What is Convolution (part 3) 00:06:41 Duration
Lecture 4 Convolution on Color Images 00:15:59 Duration
Lecture 5 CNN Architecture 00:20:58 Duration
Lecture 6 CNN Code Preparation 00:15:13 Duration
Lecture 7 CNN for Fashion MNIST 00:06:46 Duration
Lecture 8 CNN for CIFAR-10 00:04:28 Duration
Lecture 9 Data Augmentation 00:08:51 Duration
Lecture 10 Batch Normalization 00:05:14 Duration
Lecture 11 Improving CIFAR-10 Results 00:10:22 Duration

Section 6 : Natural Language Processing (NLP)
Lecture 1 Embeddings 00:13:12 Duration
Lecture 2 Code Preparation (NLP) 00:13:18 Duration
Lecture 3 Text Preprocessing 00:05:30 Duration
Lecture 4 CNNs for Text 00:08:08 Duration
Lecture 5 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM

Section 7 : Convolution In-Depth
Lecture 1 Real-Life Examples of Convolution 00:08:53 Duration
Lecture 2 Beginner's Guide to Convolution 00:06:27 Duration
Lecture 3 Alternative Views on Convolution 00:06:42 Duration

Section 8 : Convolutional Neural Network Description
Lecture 1 Convolution on 3-D Images 00:10:50 Duration
Lecture 2 Tracking Shapes in a CNN 00:16:37 Duration

Section 9 : Practical Tips
Lecture 1 Advanced CNNs and how to Design your Own 00:11:10 Duration

Section 10 : In-Depth Loss Functions
Lecture 1 Mean Squared Error 00:09:12 Duration
Lecture 2 Binary Cross Entropy 00:05:58 Duration
Lecture 3 Categorical Cross Entropy 00:08:07 Duration

Section 11 : In-Depth Gradient Descent
Lecture 1 Gradient Descent 00:07:52 Duration
Lecture 2 Stochastic Gradient Descent 00:04:37 Duration
Lecture 3 Momentum 00:06:10 Duration
Lecture 4 Variable and Adaptive Learning Rates 00:11:45 Duration
Lecture 5 Adam

Section 12 : Extras
Lecture 1 Colab Notebooks

Section 13 : Setting Up Your Environment (FAQ by Student Request)
Lecture 1 Windows-Focused Environment Setup 2018 00:20:20 Duration
Lecture 2 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 00:17:33 Duration

Section 14 : Extra Help With Python Coding for Beginners (FAQ by Student Request)
Lecture 1 How to Code by Yourself (part 1) 00:15:54 Duration
Lecture 2 How to Code by Yourself (part 2) 00:09:23 Duration
Lecture 3 How to Uncompress a 00:03:18 Duration
Lecture 4 Proof that using Jupyter Notebook is the same as not using it 00:12:29 Duration
Lecture 5 Python 2 vs Python 3 00:04:38 Duration
Lecture 6 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM

Section 15 : Effective Learning Strategies for Machine Learning (FAQ by Student Request)
Lecture 1 How to Succeed in this Course (Long Version) 00:10:24 Duration
Lecture 2 Is this for Beginners or Experts Academic or Practical Fast or slow-paced 00:22:04 Duration
Lecture 3 Machine Learning and AI Prerequisite Roadmap (pt 1)
Lecture 4 Machine Learning and AI Prerequisite Roadmap (pt 2) 00:16:07 Duration

Section 16 : Appendix FAQ Finale
Lecture 1 What is the Appendix 00:02:48 Duration
Lecture 2 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM