Comprehensive Guide to Artificial Intelligence (AI)
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- Clearly define AI and Deep Learning
- Build Convolutional Neural Network on IBM Watson for MNIST and CIFAR 10 Datasets (No coding)
- Build Supervised and Unsupervised Machine learning Models using IBM Watson (No coding)
- Test Natural Language Processing (NLP) models using IBM Watson
- Build VGG like nets, Stateful RNN nets, reuse ResNet50 using Keras
- Test Reinforcement Learning with Keras and OpenAI Gym
- Test Recurrent Neural Network (RNN) on Mathworks
- Learn to code with Python the easy way
- Test Feed Forward Neural Networks(Classification and Regression) on Tensor Flow simulator and Google Colab
- Solve popular data sets like MNIST, CIFAR 10, with CNN using Keras
- Learn a few useful and important application of popular libraries like Numpy, Pandas, Matplotlib
- Migrate Deep Neural Network models from IBM Watson to run on local your Jupyter notebook
- Apply Transfer Learning techniques such as Reusing, Retraining with Keras
- Be able to identify the positive and the negative impact that AI will create