Adventures in Machine Learning
Ben Wilson, Michael Berk
Categoria: Tecnologia
Ascolta l'ultimo episodio:
Piotr Skalski is a computer vision engineer at Roboflow. They dive deep into the world of computer vision and AI technology, exploring the utilization of video, photo, and imagery data in array processing and NLP. They discuss the challenges and opportunities presented by these fields, as well as the impact of AI technology on various industries, including civil engineering and manufacturing. Join us as we explore the fascinating intersection of technology, innovation, and the future of software development.
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- LinkedIn: Piotr Skalski
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Episodi precedenti
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174 - Unraveling AI's Impact: Computer Vision, Generative Models, and Challenges in Software Development - ML 135Thu, 30 Nov 2023
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173 - Complexity Theory - ML 134Thu, 23 Nov 2023
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172 - How To Recession Proof Your Job - BONUSThu, 23 Nov 2023
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171 - Data Watchdogs - ML 133Thu, 16 Nov 2023
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170 - Causal Analysis - ML 132Thu, 09 Nov 2023
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169 - Data Visualization and Hugging Face - ML 131Thu, 02 Nov 2023
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168 - Confidence as Data Scientist - ML 130Thu, 19 Oct 2023
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167 - A Case Study: Recommendation Engines - ML 129Thu, 05 Oct 2023
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166 - Maximizing Efficiency in ML Project Development - ML 128Thu, 21 Sep 2023
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165 - AI that Make You Better - ML 127Thu, 14 Sep 2023
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164 - Challenges for LLM Implementation - ML 126Thu, 07 Sep 2023
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163 - ML in the Cannabis Industry - ML 125Thu, 24 Aug 2023
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162 - How AI Impacts Society - ML 124Thu, 17 Aug 2023
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161 - LLMs on Azure - ML 123Thu, 03 Aug 2023
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160 - How to Create Team Utils - ML 122Fri, 21 Jul 2023
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159 - How to Get Sh*t Done - ML 121Thu, 13 Jul 2023
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158 - ML at Netflix and How to Learn Deeply - ML 120Fri, 30 Jun 2023
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157 - How to get Promoted - ML 119Fri, 23 Jun 2023
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156 - How does Search Work? - ML 118Thu, 15 Jun 2023
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155 - How to Learn a New Tool - ML 117Thu, 08 Jun 2023
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154 - The Innovation Cycle of AI - ML 116Thu, 25 May 2023
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153 - All Things Machine Learning - ML 115Thu, 11 May 2023
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152 - How to Transition from Academics to Industry - ML 114Fri, 05 May 2023
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151 - Jason Weimann - Learn Video Game Development with Chuck - BONUSFri, 21 Apr 2023
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150 - How to Make your Projects Succeed - ML 113Thu, 27 Apr 2023
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149 - How Do You Stop Hating Your Job? - BONUSThu, 13 Apr 2023
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148 - How to Think Like a Principal Architect - ML 112Thu, 13 Apr 2023
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147 - How to Transition from Software Engineer to ML Engineer - ML 111Fri, 07 Apr 2023
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146 - Machine Learning for Meeting Notes - ML 110Thu, 30 Mar 2023
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145 - Model Serving at Databricks - ML 109Mon, 27 Mar 2023
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144 - Where ML and DevOps Meet - ML 108Fri, 17 Mar 2023
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143 - How Does ChatGPT Work? - ML 107Fri, 10 Mar 2023
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142 - Machine Learning for Movie Scripts - ML 106Fri, 03 Mar 2023
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141 - ChatGPT and the Divine - ML 105Thu, 23 Feb 2023
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140 - Deep Learning for Tabular and Time Series Data - ML 104Thu, 16 Feb 2023
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139 - Notebooks vs. IDEs With Fabian Jakobs - ML 103Thu, 09 Feb 2023
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138 - How to think about Optimization - ML 102Fri, 03 Feb 2023
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137 - Protecting Your ML From Phishing And Hackers - ML 101Fri, 27 Jan 2023
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136 - The Disruptive Power of Artificial Intelligence - ML 100Thu, 19 Jan 2023
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135 - A History Of ML And How Low Code Tooling Accelerates Solution Development - ML 099Fri, 06 Jan 2023
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134 - Moving from Dev Notebooks to Production Code - ML 098Thu, 22 Dec 2022
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133 - How to Edit and Contribute to Existing Code Base - ML 097Thu, 15 Dec 2022
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132 - MLflow 2.0 And How Large-Scale Projects Are Managed In The Open Source - ML 096Thu, 01 Dec 2022
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131 - How To Recession Proof Your Job - BONUSThu, 24 Nov 2022
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130 - Should you Context Switch when Writing Code? - ML 095Thu, 24 Nov 2022
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129 - Important Questions To Ask When Scoping ML Projects - ML 094Thu, 17 Nov 2022
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128 - How To Do Research Spikes - ML 093Thu, 10 Nov 2022
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126 - How to Simplify Data Science with DagsHub Founders - ML 092Thu, 27 Oct 2022
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125 - How to Test ML Code - ML 091Thu, 20 Oct 2022
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124 - AGI, Neuron Simulators, and More with Charles Simon - ML 090Thu, 06 Oct 2022
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