English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 5h 38m | 1.84 GB
Despite being one of the biggest technical leaps in AI in decades, building an understanding in deep learning doesn’t mean you need a math degree. All it takes is the right intuitive approach, and you’ll be writing your own neural networks in pure Python in no time!
Grokking Deep Learning in Motion is a new course that takes you on a journey into the world of deep learning. Rather than just learn how to use a single library or framework, you’ll actually discover how to build these algorithms completely from scratch!
Professional instructor Beau Carnes breaks deep learning wide open, drawing together his expertise in video instruction and Andrew Trask’s unique, intuitive approach from Grokking Deep Learning! As you move through this course, you’ll learn the fundamentals of deep learning from a unique standing! Using Python, as well as Jupyter Notebooks, you’ll get stuck right in to the basics of neural prediction and learning, and teach your algorithms to visualize things like different weights. Throughout, you’ll train your neural network to be smarter, faster, and better at its job in a variety of ways, ready for the real world!
Packed with great animations and explanations that bring the world of deep learning to life in a way that just makes sense, Grokking Deep Learning in Motion is exactly what anyone needs to build an intuitive understanding of one of the hottest techniques in machine learning.
This course also works perfectly alongside the original book Grokking Deep Learning by Andrew Trask, bringing his unique way to teaching to life.
Machine learning has made remarkable progress in recent years. Deep-learning systems now enable previously impossible smart applications, revolutionizing image recognition and natural-language processing, and identifying complex patterns in data. To really get the most out of deep learning, you need to understand it inside and out, but where do you start? This course is the perfect jumping off point!
- The differences between deep and machine learning
- An introduction to neural prediction
- Building your first deep neural network
- The importance of visualization tools
- Memorization vs Generalization
- Modeling probabilities and non-linearities
This course is perfect for anyone with high school-level math and basic programming skills with a language like Python. Experience with Calculus is helpful but NOT required.
02 What you need to get started
03 What is Deep Learning and Machine Learning
04 Supervised vs. unsupervised learning
05 Parametric vs. non-parametric learning
06 Making a prediction
07 What does a Neural Network do
08 Multiple inputs
09 Multiple outputs and stacking predictions
10 Primer on NumPy
11 Compare and learn
12 Why measure error
13 Hot and cold learning
14 Gradient descent
15 Learning with gradient decent
16 The secret to learning
17 How to use a derivative to learn
19 Gradient descent learning with multiple inputs
20 Several steps of learning
21 Gradient descent with multiple outputs
22 Visualizing weight values
23 The streetlight problem
24 Building our neural network
25 Up and down pressure
26 Correlation and backpropagation
27 Linear vs. non-linear
28 Our first ‘deep’ neural network
30 Simplified visualization
31 Seeing the network predict
32 3-layer network on MNIST
33 Overfitting in Neural Networks
34 Regularization – Early Stopping and Dropout
35 Activation Function Constraints
36 Standard Activation Functions
37 Softmax and implementation in code
38 Where to go from here