Open Source Hugging face GPT (HuggingChat) vs ChatGPT!
ok, so hugging face just launched an open source competitor, always a good thing! It using the Model: OpenAssistant/oasst-sft-6-llama-30b ·
https://huggingface.co/chat/conversation/644be6523f6f5333935d03a0
Quick comparison continuing on my previous post of ChatGPT vs Auto GPT!
Scenario given to both (same):
Hello. Can you help become my educator and trainer in coming up with plan to achieve below 3 goals: 1) Help learn concepts around Artificial intelligence from beginner to advanced level in a phase wise manner decided by this chat 2) Convert this into monthly milestones 3) Write all of this plan into a .txt or .doc file as appropriate
Response from ChatGPT
Hello! I'd be happy to help you create a learning plan to achieve your goals. Here's a suggested phase-wise plan to learn AI concepts from beginner to advanced level, broken down into monthly milestones:
Phase 1: Foundations (Months 1-3) Month 1: Basic Programming and Mathematics
Learn Python programming basics (syntax, data structures, control structures)
Study linear algebra, calculus, and probability
Month 2: Introductory Machine Learning and Data Science
Learn about basic machine learning algorithms (linear regression, logistic regression, decision trees)
Study basic data manipulation and visualization using libraries like NumPy, pandas, and matplotlib
Month 3: Deep Learning Foundations
Learn about neural networks and backpropagation
Study popular deep learning frameworks like TensorFlow and PyTorch
Phase 2: Intermediate AI Concepts (Months 4-6) Month 4: Advanced Machine Learning
Learn about ensemble methods, support vector machines, and clustering algorithms
Study feature engineering and feature selection techniques
Month 5: Deep Learning Architectures
Learn about Convolutional Neural Networks (CNNs) and their applications in computer vision
Study Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and their applications in natural language processing
Month 6: Reinforcement Learning
Learn about the fundamentals of reinforcement learning
Study popular algorithms like Q-Learning, Deep Q-Networks (DQNs), and Proximal Policy Optimization (PPO)
Phase 3: Advanced AI Concepts (Months 7-9) Month 7: Generative Models
Learn about Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)
Study their applications in generating images, text, and other data
Month 8: Advanced Natural Language Processing
Learn about transformers, attention mechanisms, and BERT
Study advanced NLP tasks like sentiment analysis, summarization, and machine translation
Month 9: Ethics and AI in Practice
Learn about the ethical considerations of AI applications
Study case studies and best practices for deploying AI solutions in real-world scenarios
To create a .txt or .doc file containing this plan, you can copy the text and paste it into your preferred text editor or word processor. Save the file in the desired format (.txt or .doc) on your computer.
Response from HuggingFaceGPT
Of course! I would be happy to assist you in becoming your own artificial intelligence educator and trainer. To start, we can break down learning Artificial Intelligence (AI) into smaller phases based on your comfort and knowledge level. We can then convert these phases into monthly milestones, which will give us a clear roadmap for your AI education journey. As you progress through each phase, we can refine your plan accordingly. Finally, I can write down this entire process in either a text or document format that suits your needs. Please provide me with some more details so I can customize our plan according to your preferences and schedule.
… ok - not so impressive, but i replied “Please come up with a plan and i can share feedback on that” , and it responds - not the end is cut in response, maybe it hit its word limit of response.
Sure! Here's a proposed plan to guide you through your artificial intelligence journey from beginner to advanced levels over a period of one year. Keep in mind that this is just a rough outline, and you may need to adjust the timeline or add/subtract certain topics depending on your specific interests and pace.
Month 1 - Introduction to AI Basics:
Learn about the history and evolution of AI
Explore different types of AI systems and approaches
Study the fundamental principles behind machine learning algorithms
Begin understanding how AI can impact industries like healthcare, finance, and transportation
Key Concepts: Machine Learning, Natural Language Processing, Computer Vision, Robotics, Deep Learning
Month 2 - Practical Implementation of Basic ML Techniques:
Build simple models using popular libraries such as TensorFlow, PyTorch, Keras, etc.
Understand supervised and unsupervised learning methods at a basic level
Apply linear regression, decision trees, k-nearest neighbors, and clustering techniques to real data sets
Perform feature engineering tasks, including normalization, scaling, and encoding data features
Key Concepts: Feature Engineering, Hyperparameter Tuning, Model Evaluation Metrics, Overfitting & Regularization, Cross Validation
Month 3 - Reinforcement Learning and Optimization:
Dive deeper into reinforcement learning theories and algorithmic implementations such as Q-Learning, SARSA, Actor Critic, and Monte Carlo Tree Search
Study the tradeoffs associated with exploration vs exploitation and policy iteration vs value iteration
Gain hands-on experience implementing RL agents and comparing their performance on simulated environments
Investigate the application potential of RL in areas like autonomous driving, robotics, gaming, and recommendation systems
Key Concepts: Markov Decision Processes (MDP), Dynamic Programming, Bellman Equations, Advantage Function, Bootstrapping, Ensemble Methods, Transfer Learning
Month 4 - Specialized Topics in AI:
Select one or two niche subfields t
Next — same prompt given to both:
Assume that i am also a beginner in programming and and right now at a mid-management level handling a practice delivering eCommerce project. Adjust your plan accordingly. SYSTEM: Human feedback: Is analyzing code the best way to start learning AI for a beginner? Assume that i am also a beginner in programming and and right now at a mid-management level handling a practice delivering eCommerce project. Adjust your plan accordingly. please convert it appropriately to a monthly plan with a target of 6 months to reach from beginner level to medium level.
Response from CHatGPT
I understand your situation and time constraints. Here's an adjusted 6-month plan to help you reach a medium level in AI, with a focus on programming basics and AI applications relevant to eCommerce:
Month 1: Basic Programming and Mathematics
Learn Python programming basics (syntax, data structures, control structures)
Study linear algebra, calculus, and probability at a high level
Month 2: Introductory Data Science and Machine Learning
Learn about basic machine learning algorithms (linear regression, logistic regression, decision trees)
Study data manipulation and visualization using libraries like NumPy, pandas, and matplotlib
Explore applications of machine learning in eCommerce (recommendation systems, sales forecasting)
Month 3: Deep Learning Foundations
Learn about neural networks and backpropagation
Study TensorFlow or PyTorch, focusing on simple feedforward neural networks
Month 4: Deep Learning Applications in eCommerce
Learn about Convolutional Neural Networks (CNNs) and their applications in computer vision (image classification, object detection)
Study Natural Language Processing (NLP) techniques like tokenization, stemming, and word embeddings
Explore NLP applications in eCommerce (sentiment analysis, chatbots)
Month 5: Intermediate Machine Learning and AI Project Management
Learn about ensemble methods, support vector machines, and clustering algorithms
Study feature engineering and feature selection techniques
Understand the process of managing AI projects, including data collection, model evaluation, and deployment
Month 6: Ethics, AI in Practice, and Reinforcement Learning Basics
Learn about the ethical considerations of AI applications
Study case studies and best practices for deploying AI solutions in real-world scenarios, focusing on eCommerce
Gain a high-level understanding of reinforcement learning and its potential applications in eCommerce (dynamic pricing, personalized promotions)
This plan takes into consideration your beginner programming level and your current role in eCommerce project management. It aims to provide you with a strong foundation in AI concepts and practical applications relevant to your field. Remember that learning AI is an ongoing process, and you can continue to build upon this plan as you gain more experience and confidence.
Response from HuggingFaceGPT
Given your current responsibilities, I recommend focusing initially on gaining foundational skills in both programming and AI fundamentals while keeping yourself updated on current industry developments. This approach will allow you to effectively lead your team without getting bogged down by technical obstacles along the way. Incorporating AI education into your daily routine gradually rather than taking intensive crash courses will keep you productively balanced across personal development, work projects, and managerial duties. Here's an adapted plan breaking down key milestones every other week (monthly) for six months:
Week 1, Week 3: Foundational Math and Python Concepts
Start building your math foundation by practicing precalculus exercises online. Gradually shift focus toward learning Python basics with resources like Codecademy, Coursera, Udemy, Datacamp, etc. During this time, experiment lightly with libraries such as NumPy and Pandas to build familiarity before embarking on complex projects later on. This will lay the groundwork necessary for comprehending most mathematical manipulations utilized in training machine learning models effectively. You could consider dedicating a couple of evenings per week alongside some dedicated lunchtime sessions for ~30 minutes per day during workdays to cover this material.
Weeks 2, 5: Data Manipulation and Visualizations with Python Libraries
During your initial weeks focused on foundational math and Python basics, slowly incorporate related libraries like Matplotlib, Seaborn, Plotly, Bokeh, Altai
