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Introduction
- Gemini, ChatGPT and other impressive artificial models.
- Human and Artificial Intelligence, Turing test, brain, neurons, synapses
- Brief history of AI
- Terminology: Machine Learning (ML), Supervised vs. unsupervised learning, Neural Networks (NN), Deep Learning (DL), NLP, LLMs, Reinforcement Learning (RL).
- Typical applications of AI. How to join the revolution?
Before AI - A quick review of some classical models
- Basic statistics
- Linear regression
- PCA
- K-means
- Decision Trees
- Approximate Nearest-Neighbors
Deep Learning
- Main models and methods. Training, testing and validation.
- Using Python Notebook and Colab
- Main AI training tools: Keras and TensorFlow by Google, Pytorch
- Hardware and software infrastructure: GPUs, Cuda, Dockers
- Relevant python libraries: NumPy, Matplotlib, Pandas
- Your first Neural Network model: Detecting patterns in tabular data
Computer vision and Convolutional Neural Networks
- Visual tasks: classification, detection, and segmentation.
- Why is Machine Vision difficult?
- NN building blocks and layers: single neuron, convolutions, pooling, fully connected layers, normalization, activation functions, loss.
- Learning the model weights: the back-propagation algorithm.
- NN architectures: feed-forward, recurrent, encoder-decoder, Siamese.
- Practical open-source vision NNs: Resnet, Yolo, Clip, SAM.
- Computer Vision mini project: detecting objects in images
Natural Language Processing (NLP) using Transformers
- Large Language Models: capabilities and limitations.
- Using LLM services in your application: Google Vertex AI and Gemini APIs
- Training Large Language Models (LLMs) and fine-tuning chatbots.
- Attention mechanism and the Transformer architecture.
- Applications: summarization, content creation, virtual assistants, translation and transcription, Sentiment Analysis.
- NLP mini project: using LLM APIs for business use case.
Deep Reinforcement Learning (RL)
- Introduction: reinforcement learning vs. non-interactive supervised learning.
- Methods: Q-Learning and Deep Q-Network.
- Applications: control and robotics, automated driving, trading and finance.
- Reinforcement learning mini project: gaming.
Other applications and trends
- Time series forecasting in healthcare.
- Recommendation systems.
- Cyber security and authentication.
- Generative Adversarial Networks (GANs)
- Multimodal Generative AI: image and video generation from text
Real-life AI projects in the industry
- Choosing an open source to start with.
- Handling data: collecting, filtering, cleaning, augmenting, preprocessing
- Validation, testing and measuring the model quality.
- Using TensorBoard: Visualizing the training process, controlling convergence and overfit
- Experimenting architectures, loss functions and hyper-parameters.
- Improving model speed: optimization, models search, pruning, distillation.
- Deploying your AI models for smartphones and edge devices with TF Lite
- Deploying your AI models on Google Cloud Platform for scale and stability
- AI development lifecycle with Google Cloud tools
Responsible AI
- Personal, social, and economic implications
- Who is responsible? legal aspects
- Being fair and reducing biases
- Data privacy, compliance, and security.
- Explainable AI
- Dealing with deep fake
- Who owns the data, the models, and the code libraries?
- Dangers and opportunities for mankind
- Towards synergy of mankind and AI
Power Skills
The “Power Skills” section is all about building the practical abilities you need to succeed in your career. This chapter is packed with hands-on exercises and practical tips to help you develop and improve skills like public speaking, time and task management, teamwork, decision making, interviewing, creating a personal LinkedIn profile and resume, and using business English relevant to the hi-tech industry. This comprehensive skill development module aims to equip you with the essential tools for a successful career path.
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