English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 6h 51m | 878 MB
If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new “superpower” that will let you build AI systems that just weren’t possible a few years ago.
In this course, you will learn the foundations of deep learning. When you finish this class, you will:
- Understand the major technology trends driving Deep Learning
- Be able to build, train and apply fully connected deep neural networks
- Know how to implement efficient (vectorized) neural networks
- Understand the key parameters in a neural network’s architecture
This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions.
This is the first course of the Deep Learning Specialization.
Who is this class for:
Prerequisites: Expected: Programming: Basic Python programming skills, with the capability to work effectively with data structures. Recommended: Mathematics: Matrix vector operations and notation. Machine Learning: Understanding how to frame a machine learning problem, including how data is represented will be beneficial. If you have taken my Machine Learning Course here, you have much more than the needed level of knowledge.
Table of Contents
Welcome to the Deep Learning Specialization
1 Welcome
Introduction to Deep Learning
2 What is a neural network
3 Supervised Learning with Neural Networks
4 Why is Deep Learning taking off
5 About this Course
6 Course Resources
Heroes of Deep Learning (Optional)
7 Geoffrey Hinton interview
Logistic Regression as a Neural Network
8 Binary Classification
9 Logistic Regression
10 Logistic Regression Cost Function
11 Gradient Descent
12 Derivatives
13 More Derivative Examples
14 Computation graph
15 Derivatives with a Computation Graph
16 Logistic Regression Gradient Descent
17 Gradient Descent on m Examples
Python and Vectorization
18 Vectorization
19 More Vectorization Examples
20 Vectorizing Logistic Regression
21 Vectorizing Logistic Regression’s Gradient Output
22 Broadcasting in Python
23 A note on python numpy vectors
24 Quick tour of Jupyter iPython Notebooks
25 Explanation of logistic regression cost function (optional)
Heroes of Deep Learning (Optional)
26 Pieter Abbeel interview
Shallow Neural Network
27 Neural Networks Overview
28 Neural Network Representation
29 Computing a Neural Network’s Output
30 Vectorizing across multiple examples
31 Explanation for Vectorized Implementation
32 Activation functions
33 Why do you need non-linear activation functions
34 Derivatives of activation functions
35 Gradient descent for Neural Networks
36 Backpropagation intuition (optional)
37 Random Initialization
Heroes of Deep Learning (Optional)
38 Ian Goodfellow interview
Deep Neural Network
39 Deep L-layer neural network
40 Forward Propagation in a Deep Network
41 Getting your matrix dimensions right
42 Why deep representations
43 Building blocks of deep neural networks
44 Forward and Backward Propagation
45 Parameters vs Hyperparameters
46 What does this have to do with the brain
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