| Title: | Deep Learning in Action |
| Date: | 9:00am-4:30pm, 1/26-27(Sat, Sun) |
| Instructor: | Bhairav Mehta, Sr. Data Scientist, Apple
He has 15 years experience in Analytics and Data Science space at various fortune 100 companies. Bhairav Mehta is academician and tenured faculty at various Bay area Universities. Bhairav Mehta has taught 1000s of students in AI, ML and Big Data technologies over last 5 years. He also gives talks at Association of Computing Machinery (ACM), IEEE Computer Science society, and various AI Conferences. Bhairav Mehta has 5 graduate degrees from top institutes: MS Computer Science (GeorgiaTech), MBA (Cornell University), MS Statistics (Cornell University) |
| Course Objectives: | What do web search, speech recognition, face recognition, machine translation, autonomous driving, and automatic scheduling have in common? These are all complex real-world problems, and the goal of artificial intelligence (AI) is to tackle these with rigorous mathematical tools. In this course, you will learn the foundational principles that drive these applications and practice implementing some of these systems. Specific topics include machine learning, search, game playing, Markov decision processes, constraint satisfaction, graphical models, and logic. The main goal of the course is to equip you with the tools to tackle AI problems. |
| Course Include: |
- 2 days/12 hours instructor-led trainings
- 10 modules of lectures
- 8 hands-on code labs
- Code labs guidance
- Training materials
|
| Course Outline: |
Day 1:
9-10am: Module 1: Overview and Introduction
- Overview and Introduction
- Introduction to Neural Networks
- Current /Future Industry Trends
- Your First Neural Network
- Lab 1: Google Colab Cloud Platform Intro and Set up
10-11am: Module 2: Get Started on Machine Learning
- Introduction
- Gradient Descent, Error function
- Training Neural Network
- Supervised and unsupervised
- Tensorflow, Keras, Theano, Lasagne, Torch, Caffe introduction
- Lab 2: Tensorflow and Keras labs for classification and clustering
11-12pm: Module 3: Neural Networks
- Regularization Intro
- Neural Network Architecture and Hyper Parameter tuning
- Convolution Neural Network
- Lab 3: Tensorflow and CNN, CNN with Regularization
12-1:30pm: Lunch break
1:30-3:00pm: Module 4: More on Neural Networks
- CNN in Tensorflow
- Weight Initialization
- Auto Encoders
- Transfer Learning
- ImageNet, LeNet, Alexnet, VGGNet, Inception, ResNet
- Object Detection
- Lab 4: Auto Encoder and Transfer learning
3:00-4:30pm: Module 5: Object Detection
- Image Segmentation
- Face Detection
- Image Classification
- Lab5: Keras and TensorFlow
Day 2
9-11am: Module 6: Advanced Object Detection
- R-CNN, F R-CNN, YOLO, Mask R-CNN
- Lab6: Image Classification
- Lab7: Image Segmentation and Face detection
11-12pm: Module 7: Advanced Neural Networks
- Recurrent Neural Network Intro (RNN)
- Long Short term Memory (LSTM)
- Motivation for learning RNN and LSTM
- Lab 8: RNN and LSTM labs for Time Series
- Cloud based tools for doing object detection, image classification and applications of CNN
12-1:30pm: Lunch break
1:30-2:30pm: Module 8: Labs
- Lab 9: RNN-LSTM Labs continued
2:30-3:30pm: Module 9: Natural Language Processing
- Sequence to Sequence LSTM Chatbots and LSTM based Text Generation
- Natural Language Processing (NLP)
- Work2Vec, Word Embedding, PCA and T-SNE for Word Embedding
- Lab 10: NLP Labs
3:30-4:30pm: Module 10: Summary and Next steps
- Review and Introduction to advanced concepts
- Reinforcement Learning
- Generative Adversarial Networks
- Autonomous Driving car
|
| Who should learn: | Developers, Data Scientists who are working on machine learning, deep learning. |
| Level: | Beginner to Intermediate |
| Prerequsite: | |