2 day Deep Learning and AI Training:
Strategism is offering AI Deep Learning training:
Our mission: Strategism’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 Strategism in your Emerging Technologies training!! We will also provide interview help and placement services.
You will get USD300.00 worth of books for Free!! That are written by Bhairav Mehta and other authors.
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?
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 new AI problems you might encounter in life.
Overview and Introduction
Introduction to Neural Networks
Current /Future Industry Trends
Your First Neural Network
Google Colab Cloud Platform Intro and Set up
Gradient Descent, Error function
Training Neural Network
Tensorflow, Keras, Theano, Lasagne, Torch, Caffe introduction
Tensorflow and Keras labs for simple classification and clustering. Supervised and unsupervised.
Neural Network Architecture and Hyper Parameter tuning
Convolution Neural Network
Labs with Tensorflow and CNN, CNN with Regularization
CNN in Tensorflow
ImageNet, LeNet, Alexnet, VGGNet, Inception, ResNet
Auto Encoder and Transfer learning labs
Labs with Keras and TensorFlow
Advanced Object Detection methods: R-CNN, F R-CNN, YOLO, Mask R-CNN, Labs
Labs for Image Classification
Labs for Image Segmentation and Face detection
Recurrent Neural Network Intro (RNN)
Long Short term Memory (LSTM)
Motivation for learning RNN and LSTM
Simple RNN and LSTM labs for Time Series
Cloud based tools for doing object detection, image classification and applications of CNN
RNN-LSTM Labs continued
Natural Language Processing (NLP)
Work2Vec, Word Embedding, PCA and T-SNE for Word Embedding
Sequence to Sequence LSTM Chatbots and LSTM based Text Generation
Review and Introduction to advanced concepts in Neural Networks e.g. Reinforcement Learning, Generative Adversarial Networks, Autonomous Driving car etc.
Programmers, analysts, managers, investors, enthusiast pretty much anyone technically curious about deploying Machine Learning.
An MIT Press book
Ian Goodfellow and Yoshua Bengio and Aaron Courville
Other Reading Material:
Slides (To be delivered separately)
Following material will be provided by Bhairav Mehta (It is worth USD300.00)
4 workshop guides will be provided
Deep Learning and Python CNN
Deep Learning and Natural Langugage Processing
LSTM and RNN
Each topic includes codes and explanation step-by-step