Sequence Models (training)

Colleagues, the “Sequence Models” program is part of the Deep Learning Specialization from DeepLearning.AI. You will be equipped with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more. you will be able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering. Gain expertise in Gated Recurrent Units (GRU), Recurrent Neural Networks, Natural Language Processing, Long Short Term Memory (LSTM), and Attention Models. Skill-based lessons include: 1) Recurrent Neural Networks, 2) Natural Language Processing & Word Embeddings, 3) Sequence Models and Attention Mechanism, and 4) Transformer Networks.

Enroll today (teams & execs welcome): https://imp.i384100.net/Wyy3eM

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. You will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, and natural language processing. 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. Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering. Skills you'll gain encompass Recurrent Neural Networks, Tensorflow, Convolutional Neural Networks, Artificial Neural Networks, and Transformers. Skill-based training modules address: 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)

Generative AI for Everyone (training)

Colleagues, the “Generative AI for Everyone” program taught by AI pioneer Andrew Ng you’ll get insights into what generative AI can do, its potential, and its limitations. You’ll delve into real-world applications and learn common use cases. You’ll get hands-on time with generative AI projects to put your knowledge into action and gain insight into its impact on both business and society. Learn: What generative AI is and how it works, its common use cases, and what this technology can and cannot do. How to think through the lifecycle of a generative AI project, from conception to launch, including how to build effective prompts. The potential opportunities and risks that generative AI technologies present to individuals, businesses, and society. Gain high-demand skills in Generative AI Tools, Large Language Models, AI strategy, Generative AI and AI Productivity. Skill-based training modules include: 1) Introduction to Generative AI - How Generative AI works, LLMs as a thought partner, AI is a general purpose technology, Writing, Reading, Chatting, What LLMs can and cannot do, Tips for prompting and Image generation; 2) Generative AI Projects - Using generative AI in software applications - Trying generative AI code yourself, Lifecycle of a generative AI project, Cost intuition, Retrieval Augmented Generation (RAG), Fine-tuning,Pretraining an LLM, Choosing a model, How LLMs follow instructions: Instruction tuning and RLHF, and Tool use and agents; and 3) Generative AI in Business and Society - Day-to-day usage of web UI LLMs, Task analysis of jobs, Additional job analysis examples, New workflows and new opportunities, Teams to build generative AI software, Automation potential across sectors, Concerns about AI, Artificial General Intelligence and Responsible AI.

Enroll today (teams & execs welcome): https://imp.i384100.net/9gKrZ4

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)

Google AI Essentials (training)

Colleagues, the Google AI Essentials program is designed to help people across roles and industries get essential AI skills to boost their productivity, zero experience required. The course is taught by AI experts at Google who are working to make the technology helpful for everyone. In under 10 hours, they’ll do more than teach you about AI — they’ll show you how to actually use it in the real world. Stuck at the beginning of a project? You’ll learn how to use AI tools to generate ideas and content. Planning an event? You’ll use AI tools to help research, organize, and make more informed decisions. Drowning in a flooded inbox? You’ll use AI tools to help speed up those daily work tasks, like drafting email responses. You’ll also learn how to write effective prompts and use AI responsibly by identifying AI’s potential biases and avoiding harm. After you complete the course, you’ll earn a certificate from Google to share with your network and potential employers. By using AI as a helpful collaboration tool, you can set yourself up for success in today’s dynamic workplace — and you don’t even need programming skills to use it. Skill-based modules include: 1) Introduction to AI, 2) Maximize Productivity With AI Tools, 3) Discover the Art of Prompt Engineering, 4) Use AI Responsibly, and 5) Stay Ahead of the AI Curve. Learn generative AI tools to help develop ideas and content, make more informed decisions, and speed up daily work tasks. Write clear and specific prompts to get the output you want - you’ll apply prompting techniques to help summarize, create tag lines, and more. Use AI responsibly by identifying AI’s potential biases and avoiding harm. Develop strategies to stay up-to-date in the emerging landscape of AI. Gain high demand and highly marketable skills in Artificial Intelligence (AI), Prompt Engineering, Large Language Models (LLMs) and Generative AI.

Enroll today (teams & executives are welcome): https://tinyurl.com/49f59efr 

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)

Supervised Machine Learning: Regression and Classification

Colleagues, the “Supervised Machine Learning: Regression and Classification” is part of Machine Learning Specialization from DeepLearning.AI. You will learn to build machine learning models in Python using popular machine learning libraries NumPy & scikit-learn. Also learn to build & train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression. Acquire high-demand skills involving Linear Regression, Regularization to Avoid Overfitting, Logistic Regression for Classification, Gradient Descent, and Supervised Learning. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.). The curriculum includes: Week 1 - Introduction to Machine Learning: Applications of machine learning, What is machine learning?, Supervised learning, Unsupervised learning, Jupyter Notebooks, Linear regression model part, Cost function formula, Visualizing the cost function and examples, Gradient descent, Implementing gradient descent, Gradient descent intuition, Learning rates, Gradient descent for linear regression, and Running gradient descent; Week 2 - Regression with multiple input variables: Multiple features, Vectorization part, Gradient descent for multiple linear regression, Feature scaling part, Feature scaling part, Checking gradient descent for convergence, Choosing the learning rate, Feature engineering, and Polynomial regression; and Week 3 - Classification: Motivations, Logistic regression, Decision boundary, Cost function for logistic regression, Simplified Cost Function for Logistic Regression, Gradient Descent Implementation, The problem of overfitting, Addressing overfitting, Cost function with regularization, Regularized linear regression, and Human-Centered AI. 

Enroll today (teams & execs welcome): https://imp.i384100.net/555bxN 

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)

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)