Deep Learning Nanodegree

Deep Learning Nanodegree
Deep Learning Nanodegree
English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 18h 40m | 3.2 GB

Deep Learning: Build Deep Learning Model Today

Deep learning is driving advances in artificial intelligence that are changing our world. Enroll now to build and apply your own deep neural networks to challenges like image classification and generation, time-series prediction, and model deployment.

In this program, you’ll cover Convolutional and Recurrent Neural Networks, Generative Adversarial Networks, Deployment, and more. You’ll use PyTorch, and have access to GPUs to train models faster. This is the ideal point-of-entry into the field of AI.

Table of Contents

1 Welcome to Deep Learning
2 Anaconda
3 Applying Deep Learning
4 Jupyter Notebooks
5 Matrix Math and NumPy Refresher
6 Introduction to Neural Networks
7 Implementing Gradient Descent
8 Training Neural Networks
9 GPU Workspaces Demo
10 Project Predicting Bike Sharing Data
11 Sentiment Analysis
12 Keras
13 TensorFlow
14 Cloud Computing
15 Convolutional Neural Networks
16 CNNs in TensorFlow
17 Weight Initialization
18 Autoencoders
19 Transfer Learning in TensorFlow
20 CNN Project Dog Breed Classifier
21 Deep Learning for Cancer Detection with Sebastian Thrun
22 Recurrent Neural Networks
23 Long Short-Term Memory Networks (LSTM)
24 Implementation of RNN and LSTM
25 Hyperparameters
26 Embeddings and Word2vec
27 Sentiment Prediction RNN
28 Generate TV Scripts
29 Generative Adversarial Networks
30 Deep Convolutional GANs
31 Generate Faces
32 Semi-Supervised Learning
33 Introduction to RL
34 The RL Framework The Problem
35 The RL Framework The Solution
36 Dynamic Programming
37 Monte Carlo Methods
38 Temporal-Difference Methods
39 Solve OpenAI Gym’s Taxi-v2 Task
40 RL in Continuous Spaces
41 Deep Q-Learning
42 Policy-Based Methods
43 Actor-Critic Methods
44 Teach a Quadcopter How to Fly
45 Enroll in your next Nanodegree program
46 Evaluation Metrics
47 Regression
48 MiniFlow