30% off!! $200!! Artificial Intelligence, Machine and Deep Learning training for Computer vision, NLP, Chatbots, Self Driving cars using Tensorflow, Keras, MXNet, PyTorch

Erudition Inc. is offering AI Deep Learning training on Nov 2-3 2019.

Our mission: Erudition Inc.'s mission is to provide education in emerging technologies to masses at no cost or very affordable rate. What is life's objective at the end of the day? Life is fleeting, and permanence in this world is something we all strive for. The best way to achieve permanence is through sharing knowledge.

Doesn't matter if you are aligned to left brain or right brain you can join Erudition in your Emerging Technologies training!! We will also provide interview help and placement services.

Time Date : Nov 2-3 2019 9AM to 6PM

Location: iBridge, 215 Fourier Ave, Fremont, CA (Near Warmsprings BART)

Price: USD 200/- (30% Off!! Use Promo Code ALUMNI)

Instructor: Bhairav Mehta

Bhairav Mehta is Data Science Manager at Apple Inc. 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, Global Big Data and AI conferences, Open Data science conference and other forums. Bhairav Mehta has 5 graduate degrees from top institutes: MS Computer Science (GeorgiaTech), MBA (Cornell University), MS Statistics (Cornell University) etc.

Linkedin Profile: https://www.linkedin.com/in/mehtabhairav/­

Erudition Website: http://www.eruditionsiliconvalley.com

Bhairav Mehta talk videos and other conference proceedings: https://bit.ly/2MrMbGV­

What is this course about?

This course is project based advanced Deep Learning with Computer Vision and Natural Language Processing. We will discuss bleeding edge use cases and learn by doing projects on advanced computer vision and NLP applications. Some of the topics in discussion: Tuning Deep Learning networks, Region proposal based Convolution Neural Networks (R-CNN), Mask R-CNN, YOLO (You only Look Once). Other advanced pre-trained image recognition models. We will also discuss 8 unique Deep Learning for NLP projects and pre-trained NLP networks.

Pre-Requisite: Students are expected to know basic Machine Learning and Deep Learning concepts including introductory computer vision and NLP equivalent to my introductory AI Deep Learning class

Schedule:

Day 1: Advanced Artificial Intelligence and Deep Learning Concepts

Introduction

Industry Overview and current scope

Advanced Topics in Deep Learning for Computer Vision

Part 1: Foundations

• Lesson 01: Introduction to Computer Vision
• Lesson 02: Promise of Deep Learning for Computer Vision
• Lesson 03: How to Develop Deep Learning Models With Keras

Part 2: Image Data Preparation

• Lesson 04: How to Load and Manipulate Images with PIL/Pillow
• Lesson 05: How to Manually Scale Image Pixel Data
• Lesson 06: How to Load and Manipulate Images with Keras
• Lesson 07: How to Scale Image Pixel Data with Keras
• Lesson 08: How to Load Large Datasets From Directories with Keras
• Lesson 09: How to Use Image Data Augmentation in Keras

Part 3: Convolutions and Pooling

• Lesson 010: How to Use Different Channel Ordering Formats
• Lesson 011: How Convolutional Layers Work
• Lesson 012: How to Use Filter Size, Padding, and Stride
• Lesson 013: How Pooling Layers Work

Part 4: Convolutional Neural Networks

• Lesson 014: ImageNet, ILSVRC, and Milestone Architectures
• Lesson 015: How Milestone Model Architectural Innovations Work
• Lesson 016: How to Implement Model Architectural Innovations
• Lesson 017: How to Use 1×1 Convolutions to Manage Model Complexity
• Lesson 018: How to Use Pre-Trained Models and Transfer Learning

Part 5: Image Classification

• Lesson 19: How to Classify Black and White Photos of Clothing
• Lesson 20: How to Classify Small Photos of Objects
• Lesson 21: How to Classify Photographs of Dogs and Cats
• Lesson 22: How to Label Satellite Photographs of the Amazon Rainforest

Part 6: Object Detection

• Lesson 23: Deep Learning for Object Recognition
• Lesson 24: How to Perform Object Detection with YOLOv3
• Lesson 25: How to Perform Object Detection with Mask R-CNN
• Lesson 26: How to Develop a New Object Detection Model

Part 7: Face Recognition

• Lesson 27: Deep Learning for Face Recognition
• Lesson 28: How to Detect Faces in Photographs
• Lesson 29: How to Perform Face Identification and Verification with VGGFace2
• Lesson 30: How to Perform Face Classification with FaceNet

Part 8: Generative Adverserial Networks GAN

• Introduction
• Generative Adversarial Networks
• Input Pipelines
• GAN/ DCGAN
• Recurrent Networks
• Basic RNN Cell
• Character Language Model
• Setting Up the Data
• Creating the Model
• Loss (Premium Exclusive)
• Clipping the Gradient (Premium Exclusive)
• Training (Premium Exclusive)
• Extensions

Day 2: Advanced Deep Learning applications in Natural Language Processing

Part I. Foundations

• Lesson 01: Natural Language Processing
• Lesson 02: Deep Learning
• Lesson 03: Promise of Deep Learning for Natural Language
• Lesson 04: How to Develop Deep Learning Models With Keras

Part II. Data Preparation

• Lesson 05: How to Clean Text Manually and with NLTK
• Lesson 06: How to Prepare Text Data with scikit-learn
• Lesson 07: How to Prepare Text Data With Keras

Part III. Bag-of-Words

• Lesson 08: The Bag-of-Words Model
• Lesson 09: Prepare Movie Review Data for Sentiment Analysis
• Lesson 10: Neural Bag-of-Words Model for Sentiment Analysis

Part IV. Word Embeddings

• Lesson 11: The Word Embedding Model
• Lesson 12: How to Develop Word Embeddings with Gensim
• Lesson 13: How to Learn and Load Word Embeddings in Keras

Part V. Text Classification

• Lesson 14: Neural Models for Document Classification
• Lesson 15: Develop an Embedding + CNN Model
• Lesson 16: Develop an n-gram CNN Model for Sentiment Analysis

Part VI: Language Modeling

• Lesson 17: Neural Language Modeling
• Lesson 18: Develop a Character-Based Neural Language Model
• Lesson 19: How to Develop a Word-Based Neural Language Model
• Lesson 20: Develop a Neural Language Model for Text Generation

Part VII: Image Captioning

• Lesson 21: Neural Image Caption Generation
• Lesson 22: Neural Network Models for Caption Generation
• Lesson 23: Load and Use a Pre-Trained Object Recognition Model
• Lesson 24: How to Evaluate Generated Text With the BLEU Score
• Lesson 25: How to Prepare a Photo Caption Dataset For Modeling
• Lesson 26: Develop a Neural Image Caption Generation Model

Part VIII: Neural Machine Translation

• Lesson 27: Neural Machine Translation
• Lesson 28: Encoder-Decoder Models for NMT
• Lesson 29: Configure Encoder-Decoder Models for NMT
• Lesson 30: How to Develop a Neural Machine Translation Model

Park IX: Pretrained NLP networks

• Multi-Purpose NLP Models
  • ULMFiT
  • Transformer
  • Google’s BERT
  • Transformer-XL
  • OpenAI’s GPT-2
• Word Embeddings
  • ELMo
  • Flair
• Other Pretrained Models
  • StanfordNLP

Part IIX: Reinforcement Learning

• High Level Overview of Reinforcement Learning and Course Outline
• Return of the Multi-Armed Bandit
• Build an Intelligent Tic-Tac-Toe Agent
• Markov Decision Proccesses
• Dynamic Programming
• Monte Carlo
• Temporal Difference Learning
• Approximation Methods
• Stock Trading Project with Reinforcement Learning

Intended Audience

Programmers, analysts, managers, investors, enthusiast pretty much anyone technically curious about deploying Machine Learning.

TextBooks:

https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf

Each topic includes codes and explanation step-by-step

Thanks

Bhairav Mehta

URL: http://www.eruditionsiliconvalley.com

Phone# 4086608118

Email: eruditionbayarea@gmail.com