Divide and Conquer, Sorting and Searching, and Randomized Algorithms (training)

Colleagues, the “Divide and Conquer, Sorting and Searching, and Randomized Algorithms” program is part of the Algorithms Specialization from Stanford University with over 244k students enrolled online. The primary topics in this part of the specialization are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts). Skill-based training lessons include: Week 1 - Why Study Algorithms?, Integer Multiplication, Karatsuba Multiplication, Merge Sort: Motivation and Example, Merge Sort: Pseudocode and Analysis, Guiding Principles for Analysis of Algorithms, The Gist, Big-Oh Notation, Big Omega and Theta; Week 2 - O(n log n) Algorithm for Counting Inversions, Strassen's Subcubic Matrix Multiplication Algorithm, O(n log n) Algorithm for Closest Pair, Motivation, Formal Statement, Proof I and II, and Interpretation of the 3 Cases; Week 3 - Partitioning Around a Pivot, Correctness of Quicksort, Choosing a Good Pivot, Analysis (A Decomposition Principle, The Key Insight and Final Calculations), and Probability Review; and Week 4 - Randomized Selection (Algorithm and Analysis), Deterministic Selection - Algorithm, Omega(n log n) Lower Bound for Comparison-Based Sorting, Graphs and Minimum Cuts, Graph Representations, Random Contraction Algorithm, Analysis of Contraction Algorithm and Counting Minimum Cuts. 

Enroll today (teams & execs welcome): https://imp.i384100.net/qz4b9O 

Download your free AI-ML-DL - Career Transformation Guide.

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) or (Kindle) 

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) or (Kindle) 

Much career success, Lawrence E. Wilson - AI Academy (share with your team)

CS50 Intro to Artificial Intelligence - Python (HarvardX - training)

Colleagues, in the “CS50's Introduction to Artificial Intelligence with Python” course from HarvardX will learn to use machine learning in Python in this introductory course on artificial intelligence. This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, machine learning, large language models, and other topics in artificial intelligence as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own. You will learn graph search algorithms, adversarial search, knowledge representation, logical inference, probability theory, Bayesian networks, Markov models, constraint satisfaction, machine learning, reinforcement learning, neural networks, and natural language processing.

Enroll today (teams & execs welcome): http://edx.sjv.io/qz4bKq 

Download your free AI-ML-DL - Career Transformation Guide.

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) or (Kindle) 

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) or (Kindle) 

Much career success, Lawrence E. Wilson - AI Academy (share with your team)

“Transformative Innovation” Audio and eBook series make a wonderful Christmas gift!

Black Friday Bonanza - Audible & Kindle Book Series (Amazon)

Transformative Innovation series:

 

1 - ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity (Audible) (Kindle)

2 - ChatGPT - The Era of Generative Conversational AI Has Begun (Audible) (Kindle) 

3 - The Race for Quantum Computing  (Audible) (Kindle) 

Merry Christmas. Order today, Genesys Digital (Amazon Author Page) https://tinyurl.com/hh7bf4m9 

Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Colleagues, the “Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning” program is part of the DeepLearning.AI TensorFlow Developer Professional Certificate. Learn best practices for using TensorFlow, a popular open-source machine learning framework. Build a basic neural network in TensorFlow. Train a neural network for a computer vision application. Understand how to use convolutions to improve your neural network. Gain high-demand skills in Computer Vision, Tensorflow and Machine Learning. The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new DeepLearning.AI TensorFlow Developer Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization. Skill-based training modules include: 1) A New Programming Paradigm, 2) Introduction to Computer Vision, 3) Enhancing Vision with Convolutional Neural Networks, and 4) Using Real-world Images. 

Enroll today (teams & execs welcome): http://imp.i384100.net/21ZjKD 

Download your free AI-ML-DL - Career Transformation Guide.

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) or (Kindle) 

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) or (Kindle) 

Much career success, Lawrence E. Wilson - AI Academy (share with your team)

Deep Learning Specialization

Colleagues, in the “Deep Learning Specialization” you will master the fundamentals of deep learning and break into AI. Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications. Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow. Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data. Train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering. Acquire high-demand skills in Recurrent Neural Networks, Tensorflow, Convolutional Neural Networks, Artificial Neural Networks, Transformers, LSTMs, Dropout, BatchNorm and Xavier/He initialization. Training modules include: 1) Neural Networks and Deep Learning, 2) Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization, 3) Structuring Machine Learning Projects, 4) Convolutional Neural Networks and 5) Sequence Models.

Enroll today (teams & execs welcome): https://imp.i384100.net/EK3BjP 

Download your free AI-ML-DL - Career Transformation Guide.

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) or (Kindle) 

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) or (Kindle) 

Much career success, Lawrence E. Wilson - AI Academy (share with your team)

AI for Everyone (training)

Colleagues, the AI for Everyone course is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take. Learn the meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science, What AI realistically can--and cannot--do, How to spot opportunities to apply AI to problems in your own organization, What it feels like to build machine learning and data science projects, How to work with an AI team and build an AI strategy in your company and How to navigate ethical and societal discussions surrounding AI. Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI. Skill-based lessons include: 1) What is AI? Machine Learning?, What is data?, The terminology of AI, What makes an AI company?, What machine learning can and cannot dos, More examples of what machine learning can and cannot dos, Non-technical explanation of deep learning, 2) Building AI Projects - Workflow of a machine learning project, Workflow of a data science project, Every job function needs to learn how to use data, How to choose an AI project, How to choose an AI project, Working with an AI team, Technical tools for AI teams, 3) Building AI In Your Company - Case study: Smart speaker, Case study: Self-driving car, Example roles of an AI team, AI Transformation Playbook, AI Transformation Playbook, AI pitfalls to avoid, Taking your first step in AI, Survey of major AI application areas, Survey of major AI techniques, and 4) AI and Society - A realistic view of AI, Discrimination/Bias, Adversarial attacks on AI, Adverse uses of AI, AI and developing economies, and AI and jobs.  

Enroll today (teams & execs welcome): https://imp.i384100.net/4P4qr9 

Download your free AI-ML-DL - Career Transformation Guide.

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) or (Kindle) 

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) or (Kindle) 

Much career success, Lawrence E. Wilson - AI Academy (share with your team)

Deep Learning: Convolutional Neural Networks in Python (training)

Colleagues, in the “Deep Learning: Convolutional Neural Networks in Python” program you will learn Tensorflow, CNNs for Computer Vision, Natural Language Processing (NLP), Data Science and Machine Learning. [79 lectures - 13+ hours of training]. Understand convolution and why it's useful for Deep Learning and explain the architecture of a convolutional neural network (CNN). Implement a CNN in TensorFlow 2. Apply CNNs to challenging Image Recognition tasks and CNNs to Natural Language Processing (NLP) for Text Classification (e.g. Spam Detection, Sentiment Analysis). Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion. Skill-based training lessons include: 1) Google Colab, 2) Machine Learning and Neurons, 3) Feedforward Artificial Neural Networks, 4) Convolutional Neural Networks, 5) Natural Language Processing (NLP), 6) Convolution In-Depth, 7) Convolutional Neural Network Description, 8) Practical Tips, 9) Loss Functions, 10) Gradient Descent, 11) Setting Up Your Environment (FAQ by Student Request), 12) Extra Help With Python Coding for Beginners, and 13) Effective Learning Strategies for Machine Learning.. 

Enroll today (teams & executives are welcome): https://tinyurl.com/yjtanmaz 

Download your free AI-ML-DL - Career Transformation Guide.

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) or (Kindle) 

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) (Kindle)

Much career success, Lawrence E. Wilson - AI Academy (share with your team)